Project1_Trado_AI (1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKtS8Byr5CEN",
        "outputId": "3275dd93-c78d-4d51-9e34-657f27de75d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sb3_contrib shimmy gym stable_baselines3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cg6WG05s5ShW",
        "outputId": "6c9777bd-24ef-44b8-9a09-b334fae44b99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sb3_contrib\n",
            "  Downloading sb3_contrib-2.0.0-py3-none-any.whl (80 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m71.7/80.3 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting shimmy\n",
            "  Downloading Shimmy-1.2.1-py3-none-any.whl (37 kB)\n",
            "Requirement already satisfied: gym in /usr/local/lib/python3.10/dist-packages (0.25.2)\n",
            "Collecting stable_baselines3\n",
            "  Downloading stable_baselines3-2.0.0-py3-none-any.whl (178 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.4/178.4 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from shimmy) (1.22.4)\n",
            "Collecting gymnasium>=0.27.0 (from shimmy)\n",
            "  Downloading gymnasium-0.29.0-py3-none-any.whl (953 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m953.8/953.8 kB\u001b[0m \u001b[31m52.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gym) (2.2.1)\n",
            "Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.10/dist-packages (from gym) (0.0.8)\n",
            "  Downloading gymnasium-0.28.1-py3-none-any.whl (925 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m925.5/925.5 kB\u001b[0m \u001b[31m57.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=1.11 in /usr/local/lib/python3.10/dist-packages (from stable_baselines3) (2.0.1+cu118)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from stable_baselines3) (1.5.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from stable_baselines3) (3.7.1)\n",
            "Collecting jax-jumpy>=1.0.0 (from gymnasium>=0.27.0->shimmy)\n",
            "  Downloading jax_jumpy-1.0.0-py3-none-any.whl (20 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium>=0.27.0->shimmy) (4.7.1)\n",
            "Collecting farama-notifications>=0.0.1 (from gymnasium>=0.27.0->shimmy)\n",
            "  Downloading Farama_Notifications-0.0.4-py3-none-any.whl (2.5 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.11->stable_baselines3) (3.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11->stable_baselines3) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11->stable_baselines3) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11->stable_baselines3) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11->stable_baselines3) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.11->stable_baselines3) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.11->stable_baselines3) (16.0.6)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (4.41.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (3.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->stable_baselines3) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->stable_baselines3) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11->stable_baselines3) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11->stable_baselines3) (1.3.0)\n",
            "Installing collected packages: farama-notifications, jax-jumpy, gymnasium, shimmy, stable_baselines3, sb3_contrib\n",
            "Successfully installed farama-notifications-0.0.4 gymnasium-0.28.1 jax-jumpy-1.0.0 sb3_contrib-2.0.0 shimmy-1.2.1 stable_baselines3-2.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import gym\n",
        "from stable_baselines3.common.evaluation import evaluate_policy\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "from gym import spaces\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sb3_contrib import RecurrentPPO\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "NDqfjHyV5iHR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visit: https://suite.pycodemates.com/trading-ai-using-deep-reinforcement-learning/ for datasets and pretrained models\n",
        "\n",
        "dataset = pd.read_csv('/content/drive/MyDrive/Trado AI/Datasets/indian_stock_2.csv')"
      ],
      "metadata": {
        "id": "xeg15Wco5y_O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[:370544]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XMh21tbZJ_pW",
        "outputId": "9c26aff0-defe-44cf-d759-fa64854a13fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "njbdaoKGy2Fv",
        "outputId": "a53bb215-2e19-45e2-98ac-3e65eb37d274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        timestamp     open     high      low    close   volume\n",
              "0       2017-01-02 09:15:00+05:30   298.25   298.70   297.60   297.90   7554.0\n",
              "1       2017-01-02 09:16:00+05:30   297.90   298.20   297.60   297.60   5494.0\n",
              "2       2017-01-02 09:17:00+05:30   297.55   297.60   297.10   297.10   4286.0\n",
              "3       2017-01-02 09:18:00+05:30   297.25   297.25   297.00   297.20   4763.0\n",
              "4       2017-01-02 09:19:00+05:30   297.20   297.20   296.75   296.80   4462.0\n",
              "...                           ...      ...      ...      ...      ...      ...\n",
              "370539  2021-01-01 15:24:00+05:30  1288.30  1290.55  1287.45  1290.25  15112.0\n",
              "370540  2021-01-01 15:25:00+05:30  1290.25  1290.25  1288.70  1289.20   8354.0\n",
              "370541  2021-01-01 15:26:00+05:30  1289.15  1290.00  1288.60  1289.05   4166.0\n",
              "370542  2021-01-01 15:27:00+05:30  1289.05  1290.00  1288.60  1289.50  12556.0\n",
              "370543  2021-01-01 15:28:00+05:30  1289.55  1290.00  1288.65  1288.65  10287.0\n",
              "\n",
              "[370544 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-fb964835-350c-4445-83dc-7e7fcae56f02\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-01-02 09:15:00+05:30</td>\n",
              "      <td>298.25</td>\n",
              "      <td>298.70</td>\n",
              "      <td>297.60</td>\n",
              "      <td>297.90</td>\n",
              "      <td>7554.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-01-02 09:16:00+05:30</td>\n",
              "      <td>297.90</td>\n",
              "      <td>298.20</td>\n",
              "      <td>297.60</td>\n",
              "      <td>297.60</td>\n",
              "      <td>5494.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-01-02 09:17:00+05:30</td>\n",
              "      <td>297.55</td>\n",
              "      <td>297.60</td>\n",
              "      <td>297.10</td>\n",
              "      <td>297.10</td>\n",
              "      <td>4286.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-01-02 09:18:00+05:30</td>\n",
              "      <td>297.25</td>\n",
              "      <td>297.25</td>\n",
              "      <td>297.00</td>\n",
              "      <td>297.20</td>\n",
              "      <td>4763.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-01-02 09:19:00+05:30</td>\n",
              "      <td>297.20</td>\n",
              "      <td>297.20</td>\n",
              "      <td>296.75</td>\n",
              "      <td>296.80</td>\n",
              "      <td>4462.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370539</th>\n",
              "      <td>2021-01-01 15:24:00+05:30</td>\n",
              "      <td>1288.30</td>\n",
              "      <td>1290.55</td>\n",
              "      <td>1287.45</td>\n",
              "      <td>1290.25</td>\n",
              "      <td>15112.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370540</th>\n",
              "      <td>2021-01-01 15:25:00+05:30</td>\n",
              "      <td>1290.25</td>\n",
              "      <td>1290.25</td>\n",
              "      <td>1288.70</td>\n",
              "      <td>1289.20</td>\n",
              "      <td>8354.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370541</th>\n",
              "      <td>2021-01-01 15:26:00+05:30</td>\n",
              "      <td>1289.15</td>\n",
              "      <td>1290.00</td>\n",
              "      <td>1288.60</td>\n",
              "      <td>1289.05</td>\n",
              "      <td>4166.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370542</th>\n",
              "      <td>2021-01-01 15:27:00+05:30</td>\n",
              "      <td>1289.05</td>\n",
              "      <td>1290.00</td>\n",
              "      <td>1288.60</td>\n",
              "      <td>1289.50</td>\n",
              "      <td>12556.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370543</th>\n",
              "      <td>2021-01-01 15:28:00+05:30</td>\n",
              "      <td>1289.55</td>\n",
              "      <td>1290.00</td>\n",
              "      <td>1288.65</td>\n",
              "      <td>1288.65</td>\n",
              "      <td>10287.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>370544 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb964835-350c-4445-83dc-7e7fcae56f02')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3ad4971b-93a4-485f-9eef-40e56582d7d2\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ad4971b-93a4-485f-9eef-40e56582d7d2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3ad4971b-93a4-485f-9eef-40e56582d7d2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fb964835-350c-4445-83dc-7e7fcae56f02 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fb964835-350c-4445-83dc-7e7fcae56f02');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 590
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.reset_index(drop=True, inplace=True)\n",
        "keep_mask = (dataset.index + 1) % 5 == 0\n",
        "\n",
        "dataset = dataset[keep_mask]"
      ],
      "metadata": {
        "id": "FzSGPNhsJdOT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2688df41-fb5c-401e-b413-760bb6ece3e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "86GfZ021KLIC",
        "outputId": "7ce9310c-3ce3-45a7-e842-5533d9914606"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        timestamp     open     high      low    close   volume\n",
              "4       2017-01-02 09:19:00+05:30   297.20   297.20   296.75   296.80   4462.0\n",
              "9       2017-01-02 09:24:00+05:30   296.00   296.00   295.35   295.35   3062.0\n",
              "14      2017-01-02 09:29:00+05:30   294.90   295.20   294.70   295.20    856.0\n",
              "19      2017-01-02 09:34:00+05:30   294.45   295.20   294.45   295.00   6735.0\n",
              "24      2017-01-02 09:39:00+05:30   294.20   295.75   294.20   295.75   4418.0\n",
              "...                           ...      ...      ...      ...      ...      ...\n",
              "370519  2021-01-01 15:04:00+05:30  1289.00  1289.10  1287.00  1289.00  14602.0\n",
              "370524  2021-01-01 15:09:00+05:30  1288.40  1288.85  1287.25  1288.40  14865.0\n",
              "370529  2021-01-01 15:14:00+05:30  1285.60  1285.95  1284.15  1285.00  26761.0\n",
              "370534  2021-01-01 15:19:00+05:30  1288.50  1290.10  1288.50  1288.60  14831.0\n",
              "370539  2021-01-01 15:24:00+05:30  1288.30  1290.55  1287.45  1290.25  15112.0\n",
              "\n",
              "[74108 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-10ad9bbe-8879-45ca-ae97-a1fa6fc1c594\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-01-02 09:19:00+05:30</td>\n",
              "      <td>297.20</td>\n",
              "      <td>297.20</td>\n",
              "      <td>296.75</td>\n",
              "      <td>296.80</td>\n",
              "      <td>4462.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2017-01-02 09:24:00+05:30</td>\n",
              "      <td>296.00</td>\n",
              "      <td>296.00</td>\n",
              "      <td>295.35</td>\n",
              "      <td>295.35</td>\n",
              "      <td>3062.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2017-01-02 09:29:00+05:30</td>\n",
              "      <td>294.90</td>\n",
              "      <td>295.20</td>\n",
              "      <td>294.70</td>\n",
              "      <td>295.20</td>\n",
              "      <td>856.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2017-01-02 09:34:00+05:30</td>\n",
              "      <td>294.45</td>\n",
              "      <td>295.20</td>\n",
              "      <td>294.45</td>\n",
              "      <td>295.00</td>\n",
              "      <td>6735.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2017-01-02 09:39:00+05:30</td>\n",
              "      <td>294.20</td>\n",
              "      <td>295.75</td>\n",
              "      <td>294.20</td>\n",
              "      <td>295.75</td>\n",
              "      <td>4418.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370519</th>\n",
              "      <td>2021-01-01 15:04:00+05:30</td>\n",
              "      <td>1289.00</td>\n",
              "      <td>1289.10</td>\n",
              "      <td>1287.00</td>\n",
              "      <td>1289.00</td>\n",
              "      <td>14602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370524</th>\n",
              "      <td>2021-01-01 15:09:00+05:30</td>\n",
              "      <td>1288.40</td>\n",
              "      <td>1288.85</td>\n",
              "      <td>1287.25</td>\n",
              "      <td>1288.40</td>\n",
              "      <td>14865.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370529</th>\n",
              "      <td>2021-01-01 15:14:00+05:30</td>\n",
              "      <td>1285.60</td>\n",
              "      <td>1285.95</td>\n",
              "      <td>1284.15</td>\n",
              "      <td>1285.00</td>\n",
              "      <td>26761.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370534</th>\n",
              "      <td>2021-01-01 15:19:00+05:30</td>\n",
              "      <td>1288.50</td>\n",
              "      <td>1290.10</td>\n",
              "      <td>1288.50</td>\n",
              "      <td>1288.60</td>\n",
              "      <td>14831.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370539</th>\n",
              "      <td>2021-01-01 15:24:00+05:30</td>\n",
              "      <td>1288.30</td>\n",
              "      <td>1290.55</td>\n",
              "      <td>1287.45</td>\n",
              "      <td>1290.25</td>\n",
              "      <td>15112.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74108 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10ad9bbe-8879-45ca-ae97-a1fa6fc1c594')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3cddba2b-ba9d-42b1-b33f-ba396edfb8fc\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3cddba2b-ba9d-42b1-b33f-ba396edfb8fc')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3cddba2b-ba9d-42b1-b33f-ba396edfb8fc button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-10ad9bbe-8879-45ca-ae97-a1fa6fc1c594 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-10ad9bbe-8879-45ca-ae97-a1fa6fc1c594');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 592
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Moving average\n",
        "def calculate_ma(dataset, window):\n",
        "    dataset[\"MA\"] = dataset['Close'].rolling(window=window).mean()\n",
        "\n",
        "# Code for calculating MACD (Moving average convergence divergence)\n",
        "def calculate_macd(dataset, short_window, long_window, signal_window):\n",
        "    dataset['EMA_short'] = dataset['Close'].ewm(span=short_window, adjust=False).mean()\n",
        "    dataset['EMA_long'] = dataset['Close'].ewm(span=long_window, adjust=False).mean()\n",
        "    dataset['MACD'] = dataset['EMA_short'] - dataset['EMA_long']\n",
        "    dataset['Signal'] = dataset['MACD'].ewm(span=signal_window, adjust=False).mean()\n",
        "    dataset.drop(['EMA_short', 'EMA_long'], axis=1, inplace=True)\n",
        "\n",
        "def calculate_rsi(dataset, window):\n",
        "    diff = dataset['Close'].diff()\n",
        "    gain = diff.where(diff > 0, 0)\n",
        "    loss = -diff.where(diff < 0, 0)\n",
        "    avg_gain = gain.rolling(window=window).mean()\n",
        "    avg_loss = loss.rolling(window=window).mean()\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "    dataset['RSI'] = rsi"
      ],
      "metadata": {
        "id": "O4xUldD057VD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.rename(columns={\"close\":\"Close\", \"open\":\"Open\", \"high\":\"High\", \"low\":\"Low\"}, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ff-bctdH733S",
        "outputId": "5cda0f87-6efd-42c7-d2cd-7653b2ca30db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-594-24cde409e433>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  dataset.rename(columns={\"close\":\"Close\", \"open\":\"Open\", \"high\":\"High\", \"low\":\"Low\"}, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_df = dataset.copy()\n",
        "\n",
        "eps=0.001\n",
        "\n",
        "cleaned_df['Open'] = np.log(cleaned_df.pop('Open')+eps)\n",
        "cleaned_df['High'] = np.log(cleaned_df.pop('High')+eps)\n",
        "cleaned_df['Low'] = np.log(cleaned_df.pop('Low')+eps)\n",
        "cleaned_df['Close'] = np.log(cleaned_df.pop('Close')+eps)\n",
        "\n",
        "cleaned_df = cleaned_df.drop(['timestamp', 'volume'], axis=1)"
      ],
      "metadata": {
        "id": "rqIGicgo6fs6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "M_U0ugpo6jrV",
        "outputId": "74677576-e437-4e2a-92b1-ee16418eedb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open      High       Low     Close\n",
              "4       5.694409  5.694409  5.692893  5.693062\n",
              "9       5.690363  5.690363  5.688164  5.688164\n",
              "14      5.686640  5.687656  5.685961  5.687656\n",
              "19      5.685113  5.687656  5.685113  5.686979\n",
              "24      5.684263  5.689518  5.684263  5.689518\n",
              "...          ...       ...       ...       ...\n",
              "370519  7.161623  7.161700  7.160070  7.161623\n",
              "370524  7.161157  7.161506  7.160264  7.161157\n",
              "370529  7.158982  7.159254  7.157853  7.158515\n",
              "370534  7.161235  7.162476  7.161235  7.161312\n",
              "370539  7.161080  7.162825  7.160420  7.162592\n",
              "\n",
              "[74108 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-4fcb89b9-d5cf-46a0-bf1d-f09ebccc6b7e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.694409</td>\n",
              "      <td>5.694409</td>\n",
              "      <td>5.692893</td>\n",
              "      <td>5.693062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5.690363</td>\n",
              "      <td>5.690363</td>\n",
              "      <td>5.688164</td>\n",
              "      <td>5.688164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.686640</td>\n",
              "      <td>5.687656</td>\n",
              "      <td>5.685961</td>\n",
              "      <td>5.687656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.685113</td>\n",
              "      <td>5.687656</td>\n",
              "      <td>5.685113</td>\n",
              "      <td>5.686979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5.684263</td>\n",
              "      <td>5.689518</td>\n",
              "      <td>5.684263</td>\n",
              "      <td>5.689518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370519</th>\n",
              "      <td>7.161623</td>\n",
              "      <td>7.161700</td>\n",
              "      <td>7.160070</td>\n",
              "      <td>7.161623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370524</th>\n",
              "      <td>7.161157</td>\n",
              "      <td>7.161506</td>\n",
              "      <td>7.160264</td>\n",
              "      <td>7.161157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370529</th>\n",
              "      <td>7.158982</td>\n",
              "      <td>7.159254</td>\n",
              "      <td>7.157853</td>\n",
              "      <td>7.158515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370534</th>\n",
              "      <td>7.161235</td>\n",
              "      <td>7.162476</td>\n",
              "      <td>7.161235</td>\n",
              "      <td>7.161312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370539</th>\n",
              "      <td>7.161080</td>\n",
              "      <td>7.162825</td>\n",
              "      <td>7.160420</td>\n",
              "      <td>7.162592</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74108 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fcb89b9-d5cf-46a0-bf1d-f09ebccc6b7e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-07480ab3-39ea-42d6-993a-d7b501dbcf2a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07480ab3-39ea-42d6-993a-d7b501dbcf2a')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-07480ab3-39ea-42d6-993a-d7b501dbcf2a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4fcb89b9-d5cf-46a0-bf1d-f09ebccc6b7e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4fcb89b9-d5cf-46a0-bf1d-f09ebccc6b7e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 596
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calculate_macd(cleaned_df, 12, 26, 9)\n",
        "calculate_rsi(cleaned_df, 14)\n",
        "cleaned_df['MACD_Signal_diff'] = cleaned_df['MACD'] - cleaned_df['Signal']"
      ],
      "metadata": {
        "id": "248TEfecC39-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "MqETLar-DP5l",
        "outputId": "4c2e517a-27fa-42d8-b8f9-db4afdb28c22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open      High       Low     Close      MACD    Signal        RSI  \\\n",
              "4       5.694409  5.694409  5.692893  5.693062  0.000000  0.000000        NaN   \n",
              "9       5.690363  5.690363  5.688164  5.688164 -0.000391 -0.000078        NaN   \n",
              "14      5.686640  5.687656  5.685961  5.687656 -0.000733 -0.000209        NaN   \n",
              "19      5.685113  5.687656  5.685113  5.686979 -0.001047 -0.000377        NaN   \n",
              "24      5.684263  5.689518  5.684263  5.689518 -0.001078 -0.000517        NaN   \n",
              "...          ...       ...       ...       ...       ...       ...        ...   \n",
              "370519  7.161623  7.161700  7.160070  7.161623  0.000111  0.000112  51.508807   \n",
              "370524  7.161157  7.161506  7.160264  7.161157  0.000093  0.000108  43.307270   \n",
              "370529  7.158982  7.159254  7.157853  7.158515 -0.000134  0.000060  40.941569   \n",
              "370534  7.161235  7.162476  7.161235  7.161312 -0.000087  0.000030  51.214302   \n",
              "370539  7.161080  7.162825  7.160420  7.162592  0.000053  0.000035  52.462536   \n",
              "\n",
              "        MACD_Signal_diff  \n",
              "4           0.000000e+00  \n",
              "9          -3.125407e-04  \n",
              "14         -5.237582e-04  \n",
              "19         -6.700343e-04  \n",
              "24         -5.611180e-04  \n",
              "...                  ...  \n",
              "370519     -3.240708e-07  \n",
              "370524     -1.533127e-05  \n",
              "370529     -1.935508e-04  \n",
              "370534     -1.171134e-04  \n",
              "370539      1.832293e-05  \n",
              "\n",
              "[74108 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b45868be-ac97-43aa-a7ef-8a41dfbe7b7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>MACD</th>\n",
              "      <th>Signal</th>\n",
              "      <th>RSI</th>\n",
              "      <th>MACD_Signal_diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.694409</td>\n",
              "      <td>5.694409</td>\n",
              "      <td>5.692893</td>\n",
              "      <td>5.693062</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5.690363</td>\n",
              "      <td>5.690363</td>\n",
              "      <td>5.688164</td>\n",
              "      <td>5.688164</td>\n",
              "      <td>-0.000391</td>\n",
              "      <td>-0.000078</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-3.125407e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.686640</td>\n",
              "      <td>5.687656</td>\n",
              "      <td>5.685961</td>\n",
              "      <td>5.687656</td>\n",
              "      <td>-0.000733</td>\n",
              "      <td>-0.000209</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-5.237582e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.685113</td>\n",
              "      <td>5.687656</td>\n",
              "      <td>5.685113</td>\n",
              "      <td>5.686979</td>\n",
              "      <td>-0.001047</td>\n",
              "      <td>-0.000377</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-6.700343e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5.684263</td>\n",
              "      <td>5.689518</td>\n",
              "      <td>5.684263</td>\n",
              "      <td>5.689518</td>\n",
              "      <td>-0.001078</td>\n",
              "      <td>-0.000517</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-5.611180e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370519</th>\n",
              "      <td>7.161623</td>\n",
              "      <td>7.161700</td>\n",
              "      <td>7.160070</td>\n",
              "      <td>7.161623</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>51.508807</td>\n",
              "      <td>-3.240708e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370524</th>\n",
              "      <td>7.161157</td>\n",
              "      <td>7.161506</td>\n",
              "      <td>7.160264</td>\n",
              "      <td>7.161157</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>43.307270</td>\n",
              "      <td>-1.533127e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370529</th>\n",
              "      <td>7.158982</td>\n",
              "      <td>7.159254</td>\n",
              "      <td>7.157853</td>\n",
              "      <td>7.158515</td>\n",
              "      <td>-0.000134</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>40.941569</td>\n",
              "      <td>-1.935508e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370534</th>\n",
              "      <td>7.161235</td>\n",
              "      <td>7.162476</td>\n",
              "      <td>7.161235</td>\n",
              "      <td>7.161312</td>\n",
              "      <td>-0.000087</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>51.214302</td>\n",
              "      <td>-1.171134e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370539</th>\n",
              "      <td>7.161080</td>\n",
              "      <td>7.162825</td>\n",
              "      <td>7.160420</td>\n",
              "      <td>7.162592</td>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>52.462536</td>\n",
              "      <td>1.832293e-05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74108 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b45868be-ac97-43aa-a7ef-8a41dfbe7b7b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-76240281-0f7c-467a-9f9b-dd1879e1a328\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-76240281-0f7c-467a-9f9b-dd1879e1a328')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-76240281-0f7c-467a-9f9b-dd1879e1a328 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b45868be-ac97-43aa-a7ef-8a41dfbe7b7b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b45868be-ac97-43aa-a7ef-8a41dfbe7b7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 598
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "7w01Ch2HDV4c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "cemYEklODYyb",
        "outputId": "bcac3d6d-238e-4bd0-9af0-e35ae099a3a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open      High       Low     Close      MACD    Signal        RSI  \\\n",
              "69      5.690701  5.691882  5.690532  5.690869 -0.000497 -0.000411  45.522658   \n",
              "74      5.691545  5.692725  5.691376  5.692725 -0.000337 -0.000396  49.360296   \n",
              "79      5.691882  5.692051  5.687995  5.688672 -0.000532 -0.000423  50.995797   \n",
              "84      5.688841  5.689180  5.687826  5.687826 -0.000746 -0.000488  50.327806   \n",
              "89      5.687995  5.688334  5.687995  5.688164 -0.000878 -0.000566  52.325559   \n",
              "...          ...       ...       ...       ...       ...       ...        ...   \n",
              "370519  7.161623  7.161700  7.160070  7.161623  0.000111  0.000112  51.508807   \n",
              "370524  7.161157  7.161506  7.160264  7.161157  0.000093  0.000108  43.307270   \n",
              "370529  7.158982  7.159254  7.157853  7.158515 -0.000134  0.000060  40.941569   \n",
              "370534  7.161235  7.162476  7.161235  7.161312 -0.000087  0.000030  51.214302   \n",
              "370539  7.161080  7.162825  7.160420  7.162592  0.000053  0.000035  52.462536   \n",
              "\n",
              "        MACD_Signal_diff  \n",
              "69         -8.565489e-05  \n",
              "74          5.888686e-05  \n",
              "79         -1.086238e-04  \n",
              "84         -2.580757e-04  \n",
              "89         -3.121610e-04  \n",
              "...                  ...  \n",
              "370519     -3.240708e-07  \n",
              "370524     -1.533127e-05  \n",
              "370529     -1.935508e-04  \n",
              "370534     -1.171134e-04  \n",
              "370539      1.832293e-05  \n",
              "\n",
              "[74064 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-0fa8003b-ad54-4229-9524-cd49bf2d776a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>MACD</th>\n",
              "      <th>Signal</th>\n",
              "      <th>RSI</th>\n",
              "      <th>MACD_Signal_diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>5.690701</td>\n",
              "      <td>5.691882</td>\n",
              "      <td>5.690532</td>\n",
              "      <td>5.690869</td>\n",
              "      <td>-0.000497</td>\n",
              "      <td>-0.000411</td>\n",
              "      <td>45.522658</td>\n",
              "      <td>-8.565489e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>5.691545</td>\n",
              "      <td>5.692725</td>\n",
              "      <td>5.691376</td>\n",
              "      <td>5.692725</td>\n",
              "      <td>-0.000337</td>\n",
              "      <td>-0.000396</td>\n",
              "      <td>49.360296</td>\n",
              "      <td>5.888686e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>5.691882</td>\n",
              "      <td>5.692051</td>\n",
              "      <td>5.687995</td>\n",
              "      <td>5.688672</td>\n",
              "      <td>-0.000532</td>\n",
              "      <td>-0.000423</td>\n",
              "      <td>50.995797</td>\n",
              "      <td>-1.086238e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>5.688841</td>\n",
              "      <td>5.689180</td>\n",
              "      <td>5.687826</td>\n",
              "      <td>5.687826</td>\n",
              "      <td>-0.000746</td>\n",
              "      <td>-0.000488</td>\n",
              "      <td>50.327806</td>\n",
              "      <td>-2.580757e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>5.687995</td>\n",
              "      <td>5.688334</td>\n",
              "      <td>5.687995</td>\n",
              "      <td>5.688164</td>\n",
              "      <td>-0.000878</td>\n",
              "      <td>-0.000566</td>\n",
              "      <td>52.325559</td>\n",
              "      <td>-3.121610e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370519</th>\n",
              "      <td>7.161623</td>\n",
              "      <td>7.161700</td>\n",
              "      <td>7.160070</td>\n",
              "      <td>7.161623</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>51.508807</td>\n",
              "      <td>-3.240708e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370524</th>\n",
              "      <td>7.161157</td>\n",
              "      <td>7.161506</td>\n",
              "      <td>7.160264</td>\n",
              "      <td>7.161157</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>43.307270</td>\n",
              "      <td>-1.533127e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370529</th>\n",
              "      <td>7.158982</td>\n",
              "      <td>7.159254</td>\n",
              "      <td>7.157853</td>\n",
              "      <td>7.158515</td>\n",
              "      <td>-0.000134</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>40.941569</td>\n",
              "      <td>-1.935508e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370534</th>\n",
              "      <td>7.161235</td>\n",
              "      <td>7.162476</td>\n",
              "      <td>7.161235</td>\n",
              "      <td>7.161312</td>\n",
              "      <td>-0.000087</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>51.214302</td>\n",
              "      <td>-1.171134e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370539</th>\n",
              "      <td>7.161080</td>\n",
              "      <td>7.162825</td>\n",
              "      <td>7.160420</td>\n",
              "      <td>7.162592</td>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>52.462536</td>\n",
              "      <td>1.832293e-05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74064 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fa8003b-ad54-4229-9524-cd49bf2d776a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-d0b56e53-ef6b-4f6a-b8b3-84ab3f20fcec\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0b56e53-ef6b-4f6a-b8b3-84ab3f20fcec')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d0b56e53-ef6b-4f6a-b8b3-84ab3f20fcec button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0fa8003b-ad54-4229-9524-cd49bf2d776a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0fa8003b-ad54-4229-9524-cd49bf2d776a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 600
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gym\n",
        "import numpy as np\n",
        "\n",
        "class TradingEnv(gym.Env):\n",
        "    def __init__(self, df, initial_balance=np.log(5000+eps), max_shares=1, transaction_cost_percentage=0.00000000000000001):\n",
        "        super(TradingEnv, self).__init__()\n",
        "\n",
        "        self.df = df # Dataset\n",
        "        self.current_step = 0 # Variable for iterating through each step\n",
        "        self.max_steps = len(df) - 1 # Total number of steps / lenght of the dataset\n",
        "\n",
        "        self.initial_balance = initial_balance # Balance for the first time\n",
        "        self.max_shares = max_shares # Number of shares can be bought with the price\n",
        "        self.transaction_cost_percentage = transaction_cost_percentage # Cost for each transaction\n",
        "\n",
        "        self.action_space = gym.spaces.Discrete(3)  # Actions that the agent can take. 3 actions: Buy, Sell, Hold\n",
        "\n",
        "        # Observation space where the model can observe the data\n",
        "        self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(11,), dtype=np.float32)\n",
        "\n",
        "        self.balance = self.initial_balance # Changing balance\n",
        "        self.bought = False\n",
        "        self.sold = False\n",
        "        self.hold = False\n",
        "        self.shares_held = 0\n",
        "        self.stock_price = 0\n",
        "        self.total_profit = 0\n",
        "\n",
        "    def reset(self):\n",
        "\n",
        "        \"\"\"Reset the environment each time the agent completes iteration through the dataset\"\"\"\n",
        "\n",
        "        self.current_step = 0\n",
        "        self.balance = self.initial_balance\n",
        "        self.shares_held = 0\n",
        "        self.total_profit = 0\n",
        "        self.stock_price = self.df['Close'].iloc[self.current_step]\n",
        "        return self._get_observation()\n",
        "\n",
        "    def step(self, action):\n",
        "\n",
        "        \"\"\"Method for taking action\"\"\"\n",
        "\n",
        "        self.current_step += 1 # Iterating through the dataset features\n",
        "        self.stock_price = self.df['Close'].iloc[self.current_step]\n",
        "        reward = 0.0\n",
        "\n",
        "        if action == 0:  # Buy\n",
        "            self._buy_shares()\n",
        "\n",
        "\n",
        "\n",
        "        elif action == 1:  # Sell\n",
        "            self._sell_shares()\n",
        "            self.action = 1\n",
        "\n",
        "\n",
        "        else:\n",
        "            self.hold = True\n",
        "            reward = 0.001\n",
        "            pass\n",
        "\n",
        "        # Only calculate reward when the agent performs Buy and Sell action\n",
        "        if (self.bought or self.sold) and self.hold == False:\n",
        "            reward = self._calculate_reward()\n",
        "\n",
        "        done = self.current_step >= self.max_steps\n",
        "\n",
        "        observation = self._get_observation()\n",
        "\n",
        "        return observation, reward, done, {}\n",
        "\n",
        "    def _get_observation(self):\n",
        "\n",
        "        \"\"\"The whole observation space the agent can interact with.\n",
        "        Includes prices and technical indicators\"\"\"\n",
        "\n",
        "        observation = np.array([\n",
        "            self.df['Open'].iloc[self.current_step],\n",
        "            self.df['Low'].iloc[self.current_step],\n",
        "            self.df['High'].iloc[self.current_step],\n",
        "            self.df['Close'].iloc[self.current_step],\n",
        "            self.df['MACD'].iloc[self.current_step],\n",
        "            self.df['Signal'].iloc[self.current_step],\n",
        "            self.df['MACD_Signal_diff'].iloc[self.current_step],\n",
        "            self.df['RSI'].iloc[self.current_step],\n",
        "            self.balance,\n",
        "            self.shares_held,\n",
        "            self.stock_price,\n",
        "        ], dtype=np.float32)\n",
        "\n",
        "        return observation\n",
        "\n",
        "    def _buy_shares(self):\n",
        "\n",
        "        \"\"\"Environment conditions for a buy action\"\"\"\n",
        "\n",
        "        try:\n",
        "\n",
        "            max_shares_affordable = int(self.balance / (self.stock_price * (1 + self.transaction_cost_percentage)))\n",
        "            shares_to_buy = np.random.randint(1, max_shares_affordable + 1)  # Choose a random number of shares to buy\n",
        "            transaction_cost = shares_to_buy * self.stock_price * self.transaction_cost_percentage\n",
        "            self.balance -= (shares_to_buy * self.stock_price) + transaction_cost\n",
        "            self.shares_held += shares_to_buy\n",
        "            self.bought = True\n",
        "            self.sold = False\n",
        "            self.hold = False\n",
        "        except:\n",
        "            self.bought = False\n",
        "            pass\n",
        "\n",
        "    def _sell_shares(self):\n",
        "\n",
        "        \"\"\"Environment conditions for a sell action\"\"\"\n",
        "\n",
        "        if self.shares_held > 0:\n",
        "            shares_to_sell = np.random.randint(1, self.shares_held + 1)  # Choose a random number of shares to sell\n",
        "            self.balance += (shares_to_sell * self.stock_price) * (1 - self.transaction_cost_percentage)\n",
        "            self.shares_held -= shares_to_sell\n",
        "            self.sold = True\n",
        "            self.bought = False\n",
        "            self.hold = False\n",
        "        else:\n",
        "            self.sold = False\n",
        "            pass\n",
        "\n",
        "    def _calculate_reward(self):\n",
        "\n",
        "        \"\"\"Reward calculation based on the agent's action\"\"\"\n",
        "\n",
        "        current_balance = self.balance + (self.shares_held * self.stock_price)\n",
        "        profit = current_balance - self.initial_balance # Profit calculation based on the current balance and initial balance\n",
        "        reward = (profit - self.total_profit) * 200 # Reward scaled in terms or 100\n",
        "        self.total_profit = profit # updating profit\n",
        "        return reward\n",
        "\n",
        "\n",
        "    def render(self):\n",
        "        \"\"\"Information rendering to the screen\"\"\"\n",
        "        print(\"Stock Price:\", np.exp(self.stock_price) - eps)\n",
        "        print(\"Account Balance:\", np.exp(self.balance) - eps)\n",
        "        print(\"Number of Shares:\", self.shares_held)\n",
        "        print(\"Profit:\", np.exp(self.total_profit) - eps)\n",
        "        print(\"--------------------------\")"
      ],
      "metadata": {
        "id": "XwhyBYDvOb1z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "env = TradingEnv(cleaned_df) # Initializing the environment"
      ],
      "metadata": {
        "id": "8UMtS4h26lS3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "observation = env.reset()"
      ],
      "metadata": {
        "id": "BDVHB5P9zUij"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reset the environment\n",
        "done = False\n",
        "\n",
        "def test_env(action_):\n",
        "    action = action_  # Buy action\n",
        "    next_observation, reward, done, info = env.step(action)\n",
        "    print(\"Reward:\",reward)\n",
        "    print(\"Action:\",action)\n",
        "    #env.render()\n",
        "    observation = next_observation"
      ],
      "metadata": {
        "id": "59m8-pP6z1hT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_env(2)\n",
        "test_env(1)\n",
        "test_env(2)\n",
        "test_env(0)\n",
        "test_env(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAWUSBlkQSPs",
        "outputId": "cc92791c-e5f9-4d2b-a497-2557a5861e90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reward: 0.001\n",
            "Action: 2\n",
            "Reward: 0.0\n",
            "Action: 1\n",
            "Reward: 0.001\n",
            "Action: 2\n",
            "Reward: 0.0\n",
            "Action: 0\n",
            "Reward: -0.30495455309598185\n",
            "Action: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env.render()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oR9BkUlNYx42",
        "outputId": "3b6608e0-f9f2-443c-b354-6fac1eb169b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock Price: 294.8999999999999\n",
            "Account Balance: 4992.38194402592\n",
            "Number of Shares: 0\n",
            "Profit: 0.9974763891099062\n",
            "--------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RecurrentPPO.load('/content/drive/MyDrive/Trado AI - Deep Reinforcement Learning/Models/TradoV2.zip', env=env, ent_coef=0.70)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02xHovSBHcQv",
        "outputId": "3bba93c2-b8cb-4735-99ab-82a4b4c79063"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/stable_baselines3/common/vec_env/patch_gym.py:49: UserWarning: You provided an OpenAI Gym environment. We strongly recommend transitioning to Gymnasium environments. Stable-Baselines3 is automatically wrapping your environments in a compatibility layer, which could potentially cause issues.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model = RecurrentPPO('MlpLstmPolicy', env, verbose=1, ent_coef=0.60, device='cuda')\n",
        "model.learn(2000000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NuN2MMBX70He",
        "outputId": "649a9660-18ea-49ee-de63-09f2515a9d10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "|    value_loss           | 0.19           |\n",
            "--------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2743         |\n",
            "|    time_elapsed         | 1297         |\n",
            "|    total_timesteps      | 351104       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.149904e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0444       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.42        |\n",
            "|    n_updates            | 105550       |\n",
            "|    policy_gradient_loss | -0.000183    |\n",
            "|    value_loss           | 0.707        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2744         |\n",
            "|    time_elapsed         | 1297         |\n",
            "|    total_timesteps      | 351232       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.782008e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0773      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.712       |\n",
            "|    n_updates            | 105560       |\n",
            "|    policy_gradient_loss | -0.000269    |\n",
            "|    value_loss           | 0.124        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2745         |\n",
            "|    time_elapsed         | 1297         |\n",
            "|    total_timesteps      | 351360       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.903444e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.109       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.696       |\n",
            "|    n_updates            | 105570       |\n",
            "|    policy_gradient_loss | -7.33e-05    |\n",
            "|    value_loss           | 0.158        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2746          |\n",
            "|    time_elapsed         | 1298          |\n",
            "|    total_timesteps      | 351488        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.9348683e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00404       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.695        |\n",
            "|    n_updates            | 105580        |\n",
            "|    policy_gradient_loss | 5.63e-05      |\n",
            "|    value_loss           | 0.164         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2747          |\n",
            "|    time_elapsed         | 1298          |\n",
            "|    total_timesteps      | 351616        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.0823907e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0178        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.716        |\n",
            "|    n_updates            | 105590        |\n",
            "|    policy_gradient_loss | 8.81e-05      |\n",
            "|    value_loss           | 0.12          |\n",
            "-------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 7.41e+04    |\n",
            "|    ep_rew_mean          | 259         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 270         |\n",
            "|    iterations           | 2748        |\n",
            "|    time_elapsed         | 1299        |\n",
            "|    total_timesteps      | 351744      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 3.46452e-07 |\n",
            "|    clip_fraction        | 0           |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.1        |\n",
            "|    explained_variance   | -0.0227     |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | -0.534      |\n",
            "|    n_updates            | 105600      |\n",
            "|    policy_gradient_loss | 3.6e-05     |\n",
            "|    value_loss           | 0.496       |\n",
            "-----------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2749          |\n",
            "|    time_elapsed         | 1299          |\n",
            "|    total_timesteps      | 351872        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.5537796e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0103        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.473        |\n",
            "|    n_updates            | 105610        |\n",
            "|    policy_gradient_loss | -0.000295     |\n",
            "|    value_loss           | 0.607         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2750          |\n",
            "|    time_elapsed         | 1300          |\n",
            "|    total_timesteps      | 352000        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.4450164e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0427        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.694        |\n",
            "|    n_updates            | 105620        |\n",
            "|    policy_gradient_loss | -0.000494     |\n",
            "|    value_loss           | 0.162         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2751          |\n",
            "|    time_elapsed         | 1300          |\n",
            "|    total_timesteps      | 352128        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.6758836e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00671      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.283         |\n",
            "|    n_updates            | 105630        |\n",
            "|    policy_gradient_loss | 0.000822      |\n",
            "|    value_loss           | 2.17          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2752          |\n",
            "|    time_elapsed         | 1300          |\n",
            "|    total_timesteps      | 352256        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.1154992e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.054         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.52         |\n",
            "|    n_updates            | 105640        |\n",
            "|    policy_gradient_loss | 3.87e-05      |\n",
            "|    value_loss           | 0.514         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2753          |\n",
            "|    time_elapsed         | 1301          |\n",
            "|    total_timesteps      | 352384        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.3807314e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0501        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.418        |\n",
            "|    n_updates            | 105650        |\n",
            "|    policy_gradient_loss | 3.53e-05      |\n",
            "|    value_loss           | 0.837         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2754          |\n",
            "|    time_elapsed         | 1301          |\n",
            "|    total_timesteps      | 352512        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.9306317e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0754        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.622        |\n",
            "|    n_updates            | 105660        |\n",
            "|    policy_gradient_loss | -6.61e-05     |\n",
            "|    value_loss           | 0.297         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2755          |\n",
            "|    time_elapsed         | 1302          |\n",
            "|    total_timesteps      | 352640        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.5331974e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.084        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.66         |\n",
            "|    n_updates            | 105670        |\n",
            "|    policy_gradient_loss | -8.09e-05     |\n",
            "|    value_loss           | 0.231         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2756          |\n",
            "|    time_elapsed         | 1302          |\n",
            "|    total_timesteps      | 352768        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.5809201e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.186         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.516        |\n",
            "|    n_updates            | 105680        |\n",
            "|    policy_gradient_loss | -4.57e-05     |\n",
            "|    value_loss           | 0.577         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2757          |\n",
            "|    time_elapsed         | 1303          |\n",
            "|    total_timesteps      | 352896        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0095537e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00587       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.608        |\n",
            "|    n_updates            | 105690        |\n",
            "|    policy_gradient_loss | -4.98e-05     |\n",
            "|    value_loss           | 0.39          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2758          |\n",
            "|    time_elapsed         | 1303          |\n",
            "|    total_timesteps      | 353024        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.7923608e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00827      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.536        |\n",
            "|    n_updates            | 105700        |\n",
            "|    policy_gradient_loss | -0.000216     |\n",
            "|    value_loss           | 0.484         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2759         |\n",
            "|    time_elapsed         | 1303         |\n",
            "|    total_timesteps      | 353152       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 4.655216e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0281      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 0.556        |\n",
            "|    n_updates            | 105710       |\n",
            "|    policy_gradient_loss | 0.000357     |\n",
            "|    value_loss           | 2.7          |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2760          |\n",
            "|    time_elapsed         | 1304          |\n",
            "|    total_timesteps      | 353280        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.0563755e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00185      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.41         |\n",
            "|    n_updates            | 105720        |\n",
            "|    policy_gradient_loss | -7.6e-05      |\n",
            "|    value_loss           | 0.743         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2761          |\n",
            "|    time_elapsed         | 1304          |\n",
            "|    total_timesteps      | 353408        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.3678876e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0789        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.0933        |\n",
            "|    n_updates            | 105730        |\n",
            "|    policy_gradient_loss | 5.66e-05      |\n",
            "|    value_loss           | 1.74          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2762          |\n",
            "|    time_elapsed         | 1305          |\n",
            "|    total_timesteps      | 353536        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6344711e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00763       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.187        |\n",
            "|    n_updates            | 105740        |\n",
            "|    policy_gradient_loss | 5.12e-06      |\n",
            "|    value_loss           | 1.19          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2763          |\n",
            "|    time_elapsed         | 1305          |\n",
            "|    total_timesteps      | 353664        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.7252903e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0381        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.642        |\n",
            "|    n_updates            | 105750        |\n",
            "|    policy_gradient_loss | -6.67e-06     |\n",
            "|    value_loss           | 0.287         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2764         |\n",
            "|    time_elapsed         | 1306         |\n",
            "|    total_timesteps      | 353792       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 4.146248e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0218       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.407       |\n",
            "|    n_updates            | 105760       |\n",
            "|    policy_gradient_loss | -0.000249    |\n",
            "|    value_loss           | 0.73         |\n",
            "------------------------------------------\n",
            "---------------------------------------\n",
            "| rollout/                |           |\n",
            "|    ep_len_mean          | 7.41e+04  |\n",
            "|    ep_rew_mean          | 259       |\n",
            "| time/                   |           |\n",
            "|    fps                  | 270       |\n",
            "|    iterations           | 2765      |\n",
            "|    time_elapsed         | 1306      |\n",
            "|    total_timesteps      | 353920    |\n",
            "| train/                  |           |\n",
            "|    approx_kl            | 4.292e-06 |\n",
            "|    clip_fraction        | 0         |\n",
            "|    clip_range           | 0.2       |\n",
            "|    entropy_loss         | -1.1      |\n",
            "|    explained_variance   | 0.0299    |\n",
            "|    learning_rate        | 0.0003    |\n",
            "|    loss                 | -0.556    |\n",
            "|    n_updates            | 105770    |\n",
            "|    policy_gradient_loss | -8.43e-05 |\n",
            "|    value_loss           | 0.444     |\n",
            "---------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2766          |\n",
            "|    time_elapsed         | 1307          |\n",
            "|    total_timesteps      | 354048        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.0544976e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0174        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.531        |\n",
            "|    n_updates            | 105780        |\n",
            "|    policy_gradient_loss | -8.05e-05     |\n",
            "|    value_loss           | 0.481         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2767          |\n",
            "|    time_elapsed         | 1308          |\n",
            "|    total_timesteps      | 354176        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8868595e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00029       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.653        |\n",
            "|    n_updates            | 105790        |\n",
            "|    policy_gradient_loss | 3.7e-05       |\n",
            "|    value_loss           | 0.239         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2768          |\n",
            "|    time_elapsed         | 1308          |\n",
            "|    total_timesteps      | 354304        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.1317974e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.207         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.652        |\n",
            "|    n_updates            | 105800        |\n",
            "|    policy_gradient_loss | -0.000132     |\n",
            "|    value_loss           | 0.245         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2769          |\n",
            "|    time_elapsed         | 1309          |\n",
            "|    total_timesteps      | 354432        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.7867576e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0325        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.659         |\n",
            "|    n_updates            | 105810        |\n",
            "|    policy_gradient_loss | -0.000161     |\n",
            "|    value_loss           | 2.9           |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2770          |\n",
            "|    time_elapsed         | 1309          |\n",
            "|    total_timesteps      | 354560        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.2957406e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.015        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.326        |\n",
            "|    n_updates            | 105820        |\n",
            "|    policy_gradient_loss | -1.71e-05     |\n",
            "|    value_loss           | 0.903         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2771          |\n",
            "|    time_elapsed         | 1310          |\n",
            "|    total_timesteps      | 354688        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0384247e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00524       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.157        |\n",
            "|    n_updates            | 105830        |\n",
            "|    policy_gradient_loss | 6.37e-06      |\n",
            "|    value_loss           | 1.25          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2772         |\n",
            "|    time_elapsed         | 1310         |\n",
            "|    total_timesteps      | 354816       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 6.100163e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0204       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 1.28         |\n",
            "|    n_updates            | 105840       |\n",
            "|    policy_gradient_loss | 3.27e-06     |\n",
            "|    value_loss           | 4.17         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2773          |\n",
            "|    time_elapsed         | 1310          |\n",
            "|    total_timesteps      | 354944        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.4063275e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0267        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.169        |\n",
            "|    n_updates            | 105850        |\n",
            "|    policy_gradient_loss | -4.89e-05     |\n",
            "|    value_loss           | 1.31          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2774          |\n",
            "|    time_elapsed         | 1311          |\n",
            "|    total_timesteps      | 355072        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.9460137e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0494        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.345        |\n",
            "|    n_updates            | 105860        |\n",
            "|    policy_gradient_loss | -0.000101     |\n",
            "|    value_loss           | 0.867         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2775         |\n",
            "|    time_elapsed         | 1311         |\n",
            "|    total_timesteps      | 355200       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.775853e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.106        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.0347      |\n",
            "|    n_updates            | 105870       |\n",
            "|    policy_gradient_loss | -5.14e-05    |\n",
            "|    value_loss           | 1.62         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2776          |\n",
            "|    time_elapsed         | 1312          |\n",
            "|    total_timesteps      | 355328        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.8500875e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.11          |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 11.8          |\n",
            "|    n_updates            | 105880        |\n",
            "|    policy_gradient_loss | -0.00034      |\n",
            "|    value_loss           | 25.8          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2777          |\n",
            "|    time_elapsed         | 1312          |\n",
            "|    total_timesteps      | 355456        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.0768493e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0124       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 7.56          |\n",
            "|    n_updates            | 105890        |\n",
            "|    policy_gradient_loss | 3.71e-05      |\n",
            "|    value_loss           | 16.8          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2778          |\n",
            "|    time_elapsed         | 1313          |\n",
            "|    total_timesteps      | 355584        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2398308e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0579        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.974         |\n",
            "|    n_updates            | 105900        |\n",
            "|    policy_gradient_loss | -7.53e-05     |\n",
            "|    value_loss           | 3.51          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2779          |\n",
            "|    time_elapsed         | 1313          |\n",
            "|    total_timesteps      | 355712        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.5229529e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.107         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 3.65          |\n",
            "|    n_updates            | 105910        |\n",
            "|    policy_gradient_loss | -4.94e-05     |\n",
            "|    value_loss           | 9.08          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2780          |\n",
            "|    time_elapsed         | 1313          |\n",
            "|    total_timesteps      | 355840        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0277145e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0357        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 1.34          |\n",
            "|    n_updates            | 105920        |\n",
            "|    policy_gradient_loss | -5.12e-05     |\n",
            "|    value_loss           | 4.25          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2781         |\n",
            "|    time_elapsed         | 1314         |\n",
            "|    total_timesteps      | 355968       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.089678e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0538       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 5.64         |\n",
            "|    n_updates            | 105930       |\n",
            "|    policy_gradient_loss | 9.01e-06     |\n",
            "|    value_loss           | 13.2         |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2782         |\n",
            "|    time_elapsed         | 1314         |\n",
            "|    total_timesteps      | 356096       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.643815e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0599       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 1.36         |\n",
            "|    n_updates            | 105940       |\n",
            "|    policy_gradient_loss | -7.27e-05    |\n",
            "|    value_loss           | 4.38         |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2783         |\n",
            "|    time_elapsed         | 1315         |\n",
            "|    total_timesteps      | 356224       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 2.370216e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0148       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 3.56         |\n",
            "|    n_updates            | 105950       |\n",
            "|    policy_gradient_loss | 3.02e-05     |\n",
            "|    value_loss           | 8.87         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2784          |\n",
            "|    time_elapsed         | 1315          |\n",
            "|    total_timesteps      | 356352        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.2619725e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.128         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 7.5           |\n",
            "|    n_updates            | 105960        |\n",
            "|    policy_gradient_loss | -1.52e-05     |\n",
            "|    value_loss           | 16.8          |\n",
            "-------------------------------------------\n",
            "--------------------------------------------\n",
            "| rollout/                |                |\n",
            "|    ep_len_mean          | 7.41e+04       |\n",
            "|    ep_rew_mean          | 259            |\n",
            "| time/                   |                |\n",
            "|    fps                  | 270            |\n",
            "|    iterations           | 2785           |\n",
            "|    time_elapsed         | 1316           |\n",
            "|    total_timesteps      | 356480         |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 1.03376806e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -1.1           |\n",
            "|    explained_variance   | 0.0855         |\n",
            "|    learning_rate        | 0.0003         |\n",
            "|    loss                 | 3.22           |\n",
            "|    n_updates            | 105970         |\n",
            "|    policy_gradient_loss | -8.35e-06      |\n",
            "|    value_loss           | 8.1            |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2786          |\n",
            "|    time_elapsed         | 1316          |\n",
            "|    total_timesteps      | 356608        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.7695129e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.131         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 2.65          |\n",
            "|    n_updates            | 105980        |\n",
            "|    policy_gradient_loss | 7.87e-07      |\n",
            "|    value_loss           | 7.15          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2787          |\n",
            "|    time_elapsed         | 1317          |\n",
            "|    total_timesteps      | 356736        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.3753687e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.141         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.201         |\n",
            "|    n_updates            | 105990        |\n",
            "|    policy_gradient_loss | -0.000127     |\n",
            "|    value_loss           | 1.96          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2788         |\n",
            "|    time_elapsed         | 1317         |\n",
            "|    total_timesteps      | 356864       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.624257e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0249       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 4.36         |\n",
            "|    n_updates            | 106000       |\n",
            "|    policy_gradient_loss | 0.000266     |\n",
            "|    value_loss           | 10.5         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2789          |\n",
            "|    time_elapsed         | 1317          |\n",
            "|    total_timesteps      | 356992        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.4272975e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0365       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 4.64          |\n",
            "|    n_updates            | 106010        |\n",
            "|    policy_gradient_loss | -4.16e-05     |\n",
            "|    value_loss           | 11            |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2790          |\n",
            "|    time_elapsed         | 1318          |\n",
            "|    total_timesteps      | 357120        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1739321e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0165        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.324         |\n",
            "|    n_updates            | 106020        |\n",
            "|    policy_gradient_loss | -5.22e-05     |\n",
            "|    value_loss           | 2.24          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2791          |\n",
            "|    time_elapsed         | 1318          |\n",
            "|    total_timesteps      | 357248        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.7590431e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0275        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.261        |\n",
            "|    n_updates            | 106030        |\n",
            "|    policy_gradient_loss | -0.000148     |\n",
            "|    value_loss           | 1.05          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2792          |\n",
            "|    time_elapsed         | 1319          |\n",
            "|    total_timesteps      | 357376        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.4202669e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0269        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.499         |\n",
            "|    n_updates            | 106040        |\n",
            "|    policy_gradient_loss | -3.19e-05     |\n",
            "|    value_loss           | 2.58          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2793         |\n",
            "|    time_elapsed         | 1319         |\n",
            "|    total_timesteps      | 357504       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.648441e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.157        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 1.7          |\n",
            "|    n_updates            | 106050       |\n",
            "|    policy_gradient_loss | 1.29e-06     |\n",
            "|    value_loss           | 5.15         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2794          |\n",
            "|    time_elapsed         | 1320          |\n",
            "|    total_timesteps      | 357632        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0803342e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.21          |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.282         |\n",
            "|    n_updates            | 106060        |\n",
            "|    policy_gradient_loss | -2.05e-05     |\n",
            "|    value_loss           | 2.16          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2795          |\n",
            "|    time_elapsed         | 1320          |\n",
            "|    total_timesteps      | 357760        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3504177e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0586        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 3.76          |\n",
            "|    n_updates            | 106070        |\n",
            "|    policy_gradient_loss | -6.59e-06     |\n",
            "|    value_loss           | 9.18          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2796          |\n",
            "|    time_elapsed         | 1321          |\n",
            "|    total_timesteps      | 357888        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.8231096e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.168         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.367        |\n",
            "|    n_updates            | 106080        |\n",
            "|    policy_gradient_loss | -1.41e-05     |\n",
            "|    value_loss           | 0.922         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2797          |\n",
            "|    time_elapsed         | 1322          |\n",
            "|    total_timesteps      | 358016        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8673018e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0519       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.0671       |\n",
            "|    n_updates            | 106090        |\n",
            "|    policy_gradient_loss | -1.13e-05     |\n",
            "|    value_loss           | 1.45          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2798          |\n",
            "|    time_elapsed         | 1322          |\n",
            "|    total_timesteps      | 358144        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.1839514e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0595        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.626         |\n",
            "|    n_updates            | 106100        |\n",
            "|    policy_gradient_loss | -3.77e-05     |\n",
            "|    value_loss           | 2.97          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2799          |\n",
            "|    time_elapsed         | 1323          |\n",
            "|    total_timesteps      | 358272        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.0302832e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00202       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.773         |\n",
            "|    n_updates            | 106110        |\n",
            "|    policy_gradient_loss | -1.24e-05     |\n",
            "|    value_loss           | 3.26          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2800          |\n",
            "|    time_elapsed         | 1323          |\n",
            "|    total_timesteps      | 358400        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.5443733e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.132         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.0484        |\n",
            "|    n_updates            | 106120        |\n",
            "|    policy_gradient_loss | -8.42e-05     |\n",
            "|    value_loss           | 1.67          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2801         |\n",
            "|    time_elapsed         | 1324         |\n",
            "|    total_timesteps      | 358528       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.154195e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.343       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 0.565        |\n",
            "|    n_updates            | 106130       |\n",
            "|    policy_gradient_loss | -0.000437    |\n",
            "|    value_loss           | 2.82         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2802          |\n",
            "|    time_elapsed         | 1324          |\n",
            "|    total_timesteps      | 358656        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.8126246e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0785       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.428        |\n",
            "|    n_updates            | 106140        |\n",
            "|    policy_gradient_loss | -0.000381     |\n",
            "|    value_loss           | 0.745         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2803          |\n",
            "|    time_elapsed         | 1324          |\n",
            "|    total_timesteps      | 358784        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0159332e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0811       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.411        |\n",
            "|    n_updates            | 106150        |\n",
            "|    policy_gradient_loss | -0.000252     |\n",
            "|    value_loss           | 0.758         |\n",
            "-------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 7.41e+04    |\n",
            "|    ep_rew_mean          | 259         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 270         |\n",
            "|    iterations           | 2804        |\n",
            "|    time_elapsed         | 1325        |\n",
            "|    total_timesteps      | 358912      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 9.93954e-06 |\n",
            "|    clip_fraction        | 0           |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.1        |\n",
            "|    explained_variance   | -0.114      |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | -0.224      |\n",
            "|    n_updates            | 106160      |\n",
            "|    policy_gradient_loss | -5.86e-05   |\n",
            "|    value_loss           | 1.13        |\n",
            "-----------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2805          |\n",
            "|    time_elapsed         | 1325          |\n",
            "|    total_timesteps      | 359040        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.6729262e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0655        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.374         |\n",
            "|    n_updates            | 106170        |\n",
            "|    policy_gradient_loss | -0.000241     |\n",
            "|    value_loss           | 2.32          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2806         |\n",
            "|    time_elapsed         | 1326         |\n",
            "|    total_timesteps      | 359168       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.118083e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.126       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.156       |\n",
            "|    n_updates            | 106180       |\n",
            "|    policy_gradient_loss | -7.62e-05    |\n",
            "|    value_loss           | 1.29         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2807          |\n",
            "|    time_elapsed         | 1326          |\n",
            "|    total_timesteps      | 359296        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.7027286e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0837        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.231        |\n",
            "|    n_updates            | 106190        |\n",
            "|    policy_gradient_loss | -0.000312     |\n",
            "|    value_loss           | 1.2           |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2808         |\n",
            "|    time_elapsed         | 1327         |\n",
            "|    total_timesteps      | 359424       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 7.568393e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.16         |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 0.969        |\n",
            "|    n_updates            | 106200       |\n",
            "|    policy_gradient_loss | -0.000213    |\n",
            "|    value_loss           | 3.48         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2809          |\n",
            "|    time_elapsed         | 1327          |\n",
            "|    total_timesteps      | 359552        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.0903687e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.514        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.394        |\n",
            "|    n_updates            | 106210        |\n",
            "|    policy_gradient_loss | -0.00024      |\n",
            "|    value_loss           | 0.839         |\n",
            "-------------------------------------------\n",
            "----------------------------------------\n",
            "| rollout/                |            |\n",
            "|    ep_len_mean          | 7.41e+04   |\n",
            "|    ep_rew_mean          | 259        |\n",
            "| time/                   |            |\n",
            "|    fps                  | 270        |\n",
            "|    iterations           | 2810       |\n",
            "|    time_elapsed         | 1327       |\n",
            "|    total_timesteps      | 359680     |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 4.8941e-07 |\n",
            "|    clip_fraction        | 0          |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -1.1       |\n",
            "|    explained_variance   | 0.241      |\n",
            "|    learning_rate        | 0.0003     |\n",
            "|    loss                 | -0.136     |\n",
            "|    n_updates            | 106220     |\n",
            "|    policy_gradient_loss | 0.000174   |\n",
            "|    value_loss           | 1.42       |\n",
            "----------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2811         |\n",
            "|    time_elapsed         | 1328         |\n",
            "|    total_timesteps      | 359808       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 2.016779e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.224        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.135       |\n",
            "|    n_updates            | 106230       |\n",
            "|    policy_gradient_loss | -0.000604    |\n",
            "|    value_loss           | 1.28         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2812          |\n",
            "|    time_elapsed         | 1328          |\n",
            "|    total_timesteps      | 359936        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.7219223e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.015        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 1.11          |\n",
            "|    n_updates            | 106240        |\n",
            "|    policy_gradient_loss | -0.000215     |\n",
            "|    value_loss           | 3.84          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2813          |\n",
            "|    time_elapsed         | 1329          |\n",
            "|    total_timesteps      | 360064        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.1264499e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.165         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.21          |\n",
            "|    n_updates            | 106250        |\n",
            "|    policy_gradient_loss | -6.37e-05     |\n",
            "|    value_loss           | 2             |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2814         |\n",
            "|    time_elapsed         | 1329         |\n",
            "|    total_timesteps      | 360192       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.161328e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.397       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.554       |\n",
            "|    n_updates            | 106260       |\n",
            "|    policy_gradient_loss | -0.000524    |\n",
            "|    value_loss           | 0.485        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2815         |\n",
            "|    time_elapsed         | 1330         |\n",
            "|    total_timesteps      | 360320       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 0.0001675575 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.116        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.51        |\n",
            "|    n_updates            | 106270       |\n",
            "|    policy_gradient_loss | -0.00122     |\n",
            "|    value_loss           | 0.541        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2816          |\n",
            "|    time_elapsed         | 1330          |\n",
            "|    total_timesteps      | 360448        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.8867816e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0435        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 2.07          |\n",
            "|    n_updates            | 106280        |\n",
            "|    policy_gradient_loss | 0.000843      |\n",
            "|    value_loss           | 5.76          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2817          |\n",
            "|    time_elapsed         | 1331          |\n",
            "|    total_timesteps      | 360576        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.7506765e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0638       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.26         |\n",
            "|    n_updates            | 106290        |\n",
            "|    policy_gradient_loss | -5.29e-05     |\n",
            "|    value_loss           | 1.06          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2818          |\n",
            "|    time_elapsed         | 1331          |\n",
            "|    total_timesteps      | 360704        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.4086406e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.127         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.0437        |\n",
            "|    n_updates            | 106300        |\n",
            "|    policy_gradient_loss | 2.48e-05      |\n",
            "|    value_loss           | 1.72          |\n",
            "-------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 7.41e+04    |\n",
            "|    ep_rew_mean          | 259         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 270         |\n",
            "|    iterations           | 2819        |\n",
            "|    time_elapsed         | 1332        |\n",
            "|    total_timesteps      | 360832      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 4.02797e-07 |\n",
            "|    clip_fraction        | 0           |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.1        |\n",
            "|    explained_variance   | 0.119       |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | -0.228      |\n",
            "|    n_updates            | 106310      |\n",
            "|    policy_gradient_loss | 1.68e-06    |\n",
            "|    value_loss           | 1.1         |\n",
            "-----------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2820         |\n",
            "|    time_elapsed         | 1332         |\n",
            "|    total_timesteps      | 360960       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.721836e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.959       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.553       |\n",
            "|    n_updates            | 106320       |\n",
            "|    policy_gradient_loss | -5.55e-05    |\n",
            "|    value_loss           | 0.551        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2821         |\n",
            "|    time_elapsed         | 1333         |\n",
            "|    total_timesteps      | 361088       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 2.078712e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.348        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.463       |\n",
            "|    n_updates            | 106330       |\n",
            "|    policy_gradient_loss | -0.000142    |\n",
            "|    value_loss           | 0.618        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2822          |\n",
            "|    time_elapsed         | 1333          |\n",
            "|    total_timesteps      | 361216        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.7255155e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.114         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.972         |\n",
            "|    n_updates            | 106340        |\n",
            "|    policy_gradient_loss | -0.000115     |\n",
            "|    value_loss           | 3.63          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2823         |\n",
            "|    time_elapsed         | 1334         |\n",
            "|    total_timesteps      | 361344       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 2.394896e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.15         |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.479       |\n",
            "|    n_updates            | 106350       |\n",
            "|    policy_gradient_loss | -0.000149    |\n",
            "|    value_loss           | 0.583        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2824          |\n",
            "|    time_elapsed         | 1334          |\n",
            "|    total_timesteps      | 361472        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1296943e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0507       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.0587       |\n",
            "|    n_updates            | 106360        |\n",
            "|    policy_gradient_loss | -2.17e-06     |\n",
            "|    value_loss           | 1.56          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2825          |\n",
            "|    time_elapsed         | 1335          |\n",
            "|    total_timesteps      | 361600        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.4170073e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.142        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.169         |\n",
            "|    n_updates            | 106370        |\n",
            "|    policy_gradient_loss | -0.000109     |\n",
            "|    value_loss           | 1.9           |\n",
            "-------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 7.41e+04    |\n",
            "|    ep_rew_mean          | 259         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 270         |\n",
            "|    iterations           | 2826        |\n",
            "|    time_elapsed         | 1335        |\n",
            "|    total_timesteps      | 361728      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 2.27103e-06 |\n",
            "|    clip_fraction        | 0           |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.1        |\n",
            "|    explained_variance   | -0.0549     |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | 0.465       |\n",
            "|    n_updates            | 106380      |\n",
            "|    policy_gradient_loss | -3.29e-05   |\n",
            "|    value_loss           | 2.52        |\n",
            "-----------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2827          |\n",
            "|    time_elapsed         | 1336          |\n",
            "|    total_timesteps      | 361856        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.9404106e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00196      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.517        |\n",
            "|    n_updates            | 106390        |\n",
            "|    policy_gradient_loss | -8.48e-06     |\n",
            "|    value_loss           | 0.504         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2828          |\n",
            "|    time_elapsed         | 1336          |\n",
            "|    total_timesteps      | 361984        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.6871365e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00904       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.61         |\n",
            "|    n_updates            | 106400        |\n",
            "|    policy_gradient_loss | -0.000176     |\n",
            "|    value_loss           | 0.328         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2829         |\n",
            "|    time_elapsed         | 1337         |\n",
            "|    total_timesteps      | 362112       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 7.456634e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0014       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.361       |\n",
            "|    n_updates            | 106410       |\n",
            "|    policy_gradient_loss | -0.000137    |\n",
            "|    value_loss           | 0.887        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2830          |\n",
            "|    time_elapsed         | 1337          |\n",
            "|    total_timesteps      | 362240        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.5648624e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0111        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.268         |\n",
            "|    n_updates            | 106420        |\n",
            "|    policy_gradient_loss | -6.2e-05      |\n",
            "|    value_loss           | 2.08          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2831          |\n",
            "|    time_elapsed         | 1338          |\n",
            "|    total_timesteps      | 362368        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1362135e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0327        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.464        |\n",
            "|    n_updates            | 106430        |\n",
            "|    policy_gradient_loss | -8.51e-05     |\n",
            "|    value_loss           | 0.635         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2832          |\n",
            "|    time_elapsed         | 1338          |\n",
            "|    total_timesteps      | 362496        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0430813e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0246        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.418        |\n",
            "|    n_updates            | 106440        |\n",
            "|    policy_gradient_loss | 5.06e-05      |\n",
            "|    value_loss           | 0.769         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2833          |\n",
            "|    time_elapsed         | 1339          |\n",
            "|    total_timesteps      | 362624        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.5041164e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0119        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.459        |\n",
            "|    n_updates            | 106450        |\n",
            "|    policy_gradient_loss | 1.53e-05      |\n",
            "|    value_loss           | 0.656         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2834         |\n",
            "|    time_elapsed         | 1339         |\n",
            "|    total_timesteps      | 362752       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 9.806827e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.00285     |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.42        |\n",
            "|    n_updates            | 106460       |\n",
            "|    policy_gradient_loss | -3.87e-05    |\n",
            "|    value_loss           | 0.783        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2835         |\n",
            "|    time_elapsed         | 1339         |\n",
            "|    total_timesteps      | 362880       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 7.474795e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0372       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.485       |\n",
            "|    n_updates            | 106470       |\n",
            "|    policy_gradient_loss | -5.73e-05    |\n",
            "|    value_loss           | 0.576        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2836          |\n",
            "|    time_elapsed         | 1340          |\n",
            "|    total_timesteps      | 363008        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.3618417e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0254        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.582        |\n",
            "|    n_updates            | 106480        |\n",
            "|    policy_gradient_loss | -0.000738     |\n",
            "|    value_loss           | 0.387         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2837          |\n",
            "|    time_elapsed         | 1340          |\n",
            "|    total_timesteps      | 363136        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.3748726e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0551       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.567        |\n",
            "|    n_updates            | 106490        |\n",
            "|    policy_gradient_loss | 0.000275      |\n",
            "|    value_loss           | 0.415         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2838          |\n",
            "|    time_elapsed         | 1341          |\n",
            "|    total_timesteps      | 363264        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6636291e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.274         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.167        |\n",
            "|    n_updates            | 106500        |\n",
            "|    policy_gradient_loss | -0.000407     |\n",
            "|    value_loss           | 1.31          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2839         |\n",
            "|    time_elapsed         | 1341         |\n",
            "|    total_timesteps      | 363392       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.745119e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0566       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.0744      |\n",
            "|    n_updates            | 106510       |\n",
            "|    policy_gradient_loss | -1.98e-05    |\n",
            "|    value_loss           | 1.39         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2840          |\n",
            "|    time_elapsed         | 1342          |\n",
            "|    total_timesteps      | 363520        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.9802322e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.061         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.552        |\n",
            "|    n_updates            | 106520        |\n",
            "|    policy_gradient_loss | -9.06e-06     |\n",
            "|    value_loss           | 0.46          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2841         |\n",
            "|    time_elapsed         | 1342         |\n",
            "|    total_timesteps      | 363648       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 7.050112e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0995       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.637       |\n",
            "|    n_updates            | 106530       |\n",
            "|    policy_gradient_loss | -4.91e-05    |\n",
            "|    value_loss           | 0.279        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2842         |\n",
            "|    time_elapsed         | 1342         |\n",
            "|    total_timesteps      | 363776       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 9.465497e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.00663     |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.578       |\n",
            "|    n_updates            | 106540       |\n",
            "|    policy_gradient_loss | -0.000158    |\n",
            "|    value_loss           | 0.402        |\n",
            "------------------------------------------\n",
            "--------------------------------------------\n",
            "| rollout/                |                |\n",
            "|    ep_len_mean          | 7.41e+04       |\n",
            "|    ep_rew_mean          | 259            |\n",
            "| time/                   |                |\n",
            "|    fps                  | 270            |\n",
            "|    iterations           | 2843           |\n",
            "|    time_elapsed         | 1343           |\n",
            "|    total_timesteps      | 363904         |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 1.25113875e-05 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -1.1           |\n",
            "|    explained_variance   | 0.372          |\n",
            "|    learning_rate        | 0.0003         |\n",
            "|    loss                 | -0.547         |\n",
            "|    n_updates            | 106550         |\n",
            "|    policy_gradient_loss | -0.000568      |\n",
            "|    value_loss           | 0.52           |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2844          |\n",
            "|    time_elapsed         | 1343          |\n",
            "|    total_timesteps      | 364032        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.5438763e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.188        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.57         |\n",
            "|    n_updates            | 106560        |\n",
            "|    policy_gradient_loss | -0.00112      |\n",
            "|    value_loss           | 0.407         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2845          |\n",
            "|    time_elapsed         | 1344          |\n",
            "|    total_timesteps      | 364160        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.2763776e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0885        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 1.25          |\n",
            "|    n_updates            | 106570        |\n",
            "|    policy_gradient_loss | 0.000129      |\n",
            "|    value_loss           | 4.1           |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2846          |\n",
            "|    time_elapsed         | 1344          |\n",
            "|    total_timesteps      | 364288        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.1083043e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0736        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.0914       |\n",
            "|    n_updates            | 106580        |\n",
            "|    policy_gradient_loss | 1e-05         |\n",
            "|    value_loss           | 1.47          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2847         |\n",
            "|    time_elapsed         | 1345         |\n",
            "|    total_timesteps      | 364416       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.958121e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0994      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.301       |\n",
            "|    n_updates            | 106590       |\n",
            "|    policy_gradient_loss | -1.22e-05    |\n",
            "|    value_loss           | 0.957        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2848         |\n",
            "|    time_elapsed         | 1345         |\n",
            "|    total_timesteps      | 364544       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.504086e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.306        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 0.235        |\n",
            "|    n_updates            | 106600       |\n",
            "|    policy_gradient_loss | -2.06e-05    |\n",
            "|    value_loss           | 2.12         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2849          |\n",
            "|    time_elapsed         | 1346          |\n",
            "|    total_timesteps      | 364672        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6968697e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0352        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.0947       |\n",
            "|    n_updates            | 106610        |\n",
            "|    policy_gradient_loss | -8.08e-05     |\n",
            "|    value_loss           | 1.37          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2850         |\n",
            "|    time_elapsed         | 1346         |\n",
            "|    total_timesteps      | 364800       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.051354e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.133        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.334       |\n",
            "|    n_updates            | 106620       |\n",
            "|    policy_gradient_loss | -0.000566    |\n",
            "|    value_loss           | 0.884        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2851         |\n",
            "|    time_elapsed         | 1347         |\n",
            "|    total_timesteps      | 364928       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 6.717909e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0958       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.24        |\n",
            "|    n_updates            | 106630       |\n",
            "|    policy_gradient_loss | 0.00035      |\n",
            "|    value_loss           | 1.1          |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2852          |\n",
            "|    time_elapsed         | 1347          |\n",
            "|    total_timesteps      | 365056        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0291114e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.246        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.412        |\n",
            "|    n_updates            | 106640        |\n",
            "|    policy_gradient_loss | 5.24e-06      |\n",
            "|    value_loss           | 0.763         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2853          |\n",
            "|    time_elapsed         | 1348          |\n",
            "|    total_timesteps      | 365184        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2943132e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0661        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.232        |\n",
            "|    n_updates            | 106650        |\n",
            "|    policy_gradient_loss | -0.000265     |\n",
            "|    value_loss           | 1.17          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2854          |\n",
            "|    time_elapsed         | 1348          |\n",
            "|    total_timesteps      | 365312        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0311138e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.032         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.165        |\n",
            "|    n_updates            | 106660        |\n",
            "|    policy_gradient_loss | -1.11e-05     |\n",
            "|    value_loss           | 1.25          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2855         |\n",
            "|    time_elapsed         | 1349         |\n",
            "|    total_timesteps      | 365440       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.064517e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.143        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.108       |\n",
            "|    n_updates            | 106670       |\n",
            "|    policy_gradient_loss | -4.98e-05    |\n",
            "|    value_loss           | 1.38         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2856          |\n",
            "|    time_elapsed         | 1349          |\n",
            "|    total_timesteps      | 365568        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.2969124e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0175        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 2.95          |\n",
            "|    n_updates            | 106680        |\n",
            "|    policy_gradient_loss | -1.07e-05     |\n",
            "|    value_loss           | 7.51          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2857          |\n",
            "|    time_elapsed         | 1350          |\n",
            "|    total_timesteps      | 365696        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.6647543e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.221        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.187        |\n",
            "|    n_updates            | 106690        |\n",
            "|    policy_gradient_loss | -3.91e-05     |\n",
            "|    value_loss           | 1.19          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2858          |\n",
            "|    time_elapsed         | 1350          |\n",
            "|    total_timesteps      | 365824        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.4493784e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0958        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.489         |\n",
            "|    n_updates            | 106700        |\n",
            "|    policy_gradient_loss | -3.72e-07     |\n",
            "|    value_loss           | 2.64          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2859          |\n",
            "|    time_elapsed         | 1351          |\n",
            "|    total_timesteps      | 365952        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.2079406e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.104         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.428        |\n",
            "|    n_updates            | 106710        |\n",
            "|    policy_gradient_loss | -7.75e-05     |\n",
            "|    value_loss           | 0.692         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2860         |\n",
            "|    time_elapsed         | 1351         |\n",
            "|    total_timesteps      | 366080       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.958121e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.173        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.298       |\n",
            "|    n_updates            | 106720       |\n",
            "|    policy_gradient_loss | -4.3e-05     |\n",
            "|    value_loss           | 0.978        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2861          |\n",
            "|    time_elapsed         | 1352          |\n",
            "|    total_timesteps      | 366208        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.7232024e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0817        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.561        |\n",
            "|    n_updates            | 106730        |\n",
            "|    policy_gradient_loss | -0.00012      |\n",
            "|    value_loss           | 0.458         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2862          |\n",
            "|    time_elapsed         | 1352          |\n",
            "|    total_timesteps      | 366336        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.8754737e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.205        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.63         |\n",
            "|    n_updates            | 106740        |\n",
            "|    policy_gradient_loss | -0.000106     |\n",
            "|    value_loss           | 0.313         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2863          |\n",
            "|    time_elapsed         | 1353          |\n",
            "|    total_timesteps      | 366464        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.1110983e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0297        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.788         |\n",
            "|    n_updates            | 106750        |\n",
            "|    policy_gradient_loss | -2.25e-05     |\n",
            "|    value_loss           | 3.16          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2864          |\n",
            "|    time_elapsed         | 1353          |\n",
            "|    total_timesteps      | 366592        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.9187954e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0759        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.258        |\n",
            "|    n_updates            | 106760        |\n",
            "|    policy_gradient_loss | -0.000166     |\n",
            "|    value_loss           | 1.08          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2865         |\n",
            "|    time_elapsed         | 1353         |\n",
            "|    total_timesteps      | 366720       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 9.949785e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.044       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.495       |\n",
            "|    n_updates            | 106770       |\n",
            "|    policy_gradient_loss | -9.72e-06    |\n",
            "|    value_loss           | 0.544        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2866          |\n",
            "|    time_elapsed         | 1354          |\n",
            "|    total_timesteps      | 366848        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.0464027e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.131        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.231        |\n",
            "|    n_updates            | 106780        |\n",
            "|    policy_gradient_loss | -0.000714     |\n",
            "|    value_loss           | 1.12          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2867          |\n",
            "|    time_elapsed         | 1354          |\n",
            "|    total_timesteps      | 366976        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6344711e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.123         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.161        |\n",
            "|    n_updates            | 106790        |\n",
            "|    policy_gradient_loss | 6.76e-05      |\n",
            "|    value_loss           | 1.27          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2868          |\n",
            "|    time_elapsed         | 1355          |\n",
            "|    total_timesteps      | 367104        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.8545037e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0882        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.192        |\n",
            "|    n_updates            | 106800        |\n",
            "|    policy_gradient_loss | -3.54e-05     |\n",
            "|    value_loss           | 1.17          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2869          |\n",
            "|    time_elapsed         | 1355          |\n",
            "|    total_timesteps      | 367232        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.9697472e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0373        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.546        |\n",
            "|    n_updates            | 106810        |\n",
            "|    policy_gradient_loss | -1.6e-05      |\n",
            "|    value_loss           | 0.477         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2870          |\n",
            "|    time_elapsed         | 1356          |\n",
            "|    total_timesteps      | 367360        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.9765374e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0206       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.125        |\n",
            "|    n_updates            | 106820        |\n",
            "|    policy_gradient_loss | -0.000464     |\n",
            "|    value_loss           | 1.31          |\n",
            "-------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 7.41e+04    |\n",
            "|    ep_rew_mean          | 259         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 270         |\n",
            "|    iterations           | 2871        |\n",
            "|    time_elapsed         | 1356        |\n",
            "|    total_timesteps      | 367488      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 5.95022e-06 |\n",
            "|    clip_fraction        | 0           |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.1        |\n",
            "|    explained_variance   | 0.0794      |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | -0.462      |\n",
            "|    n_updates            | 106830      |\n",
            "|    policy_gradient_loss | 4.46e-05    |\n",
            "|    value_loss           | 0.722       |\n",
            "-----------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2872          |\n",
            "|    time_elapsed         | 1356          |\n",
            "|    total_timesteps      | 367616        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.0070306e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.225         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.37         |\n",
            "|    n_updates            | 106840        |\n",
            "|    policy_gradient_loss | -1.97e-05     |\n",
            "|    value_loss           | 0.962         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2873          |\n",
            "|    time_elapsed         | 1357          |\n",
            "|    total_timesteps      | 367744        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.0274892e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0404        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.0676        |\n",
            "|    n_updates            | 106850        |\n",
            "|    policy_gradient_loss | -0.000214     |\n",
            "|    value_loss           | 1.86          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2874         |\n",
            "|    time_elapsed         | 1357         |\n",
            "|    total_timesteps      | 367872       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 7.157214e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.229       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.269       |\n",
            "|    n_updates            | 106860       |\n",
            "|    policy_gradient_loss | -1.65e-06    |\n",
            "|    value_loss           | 1.04         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2875          |\n",
            "|    time_elapsed         | 1358          |\n",
            "|    total_timesteps      | 368000        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.4675392e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0565        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.292        |\n",
            "|    n_updates            | 106870        |\n",
            "|    policy_gradient_loss | -0.000108     |\n",
            "|    value_loss           | 0.984         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2876          |\n",
            "|    time_elapsed         | 1358          |\n",
            "|    total_timesteps      | 368128        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.9744039e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.204        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.569        |\n",
            "|    n_updates            | 106880        |\n",
            "|    policy_gradient_loss | 7.74e-05      |\n",
            "|    value_loss           | 0.442         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2877          |\n",
            "|    time_elapsed         | 1359          |\n",
            "|    total_timesteps      | 368256        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.0240196e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.153         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.485        |\n",
            "|    n_updates            | 106890        |\n",
            "|    policy_gradient_loss | -0.000141     |\n",
            "|    value_loss           | 0.584         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2878          |\n",
            "|    time_elapsed         | 1359          |\n",
            "|    total_timesteps      | 368384        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.0508028e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.213         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.398        |\n",
            "|    n_updates            | 106900        |\n",
            "|    policy_gradient_loss | -9.49e-05     |\n",
            "|    value_loss           | 0.777         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2879          |\n",
            "|    time_elapsed         | 1360          |\n",
            "|    total_timesteps      | 368512        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6698614e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.288         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.496        |\n",
            "|    n_updates            | 106910        |\n",
            "|    policy_gradient_loss | -3.51e-05     |\n",
            "|    value_loss           | 0.581         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2880          |\n",
            "|    time_elapsed         | 1360          |\n",
            "|    total_timesteps      | 368640        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6309863e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.308         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.439        |\n",
            "|    n_updates            | 106920        |\n",
            "|    policy_gradient_loss | -6.83e-06     |\n",
            "|    value_loss           | 0.754         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2881          |\n",
            "|    time_elapsed         | 1361          |\n",
            "|    total_timesteps      | 368768        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0575168e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.349        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.502        |\n",
            "|    n_updates            | 106930        |\n",
            "|    policy_gradient_loss | -9.23e-05     |\n",
            "|    value_loss           | 0.597         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2882          |\n",
            "|    time_elapsed         | 1361          |\n",
            "|    total_timesteps      | 368896        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8700957e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.167        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.468        |\n",
            "|    n_updates            | 106940        |\n",
            "|    policy_gradient_loss | -4.94e-05     |\n",
            "|    value_loss           | 0.685         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2883          |\n",
            "|    time_elapsed         | 1362          |\n",
            "|    total_timesteps      | 369024        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.0496462e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0138        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.381        |\n",
            "|    n_updates            | 106950        |\n",
            "|    policy_gradient_loss | -5.53e-05     |\n",
            "|    value_loss           | 0.794         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2884         |\n",
            "|    time_elapsed         | 1362         |\n",
            "|    total_timesteps      | 369152       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.061723e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0399       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.677       |\n",
            "|    n_updates            | 106960       |\n",
            "|    policy_gradient_loss | 4.17e-05     |\n",
            "|    value_loss           | 0.214        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2885         |\n",
            "|    time_elapsed         | 1363         |\n",
            "|    total_timesteps      | 369280       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.678724e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0772      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.712       |\n",
            "|    n_updates            | 106970       |\n",
            "|    policy_gradient_loss | 6.29e-06     |\n",
            "|    value_loss           | 0.12         |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2886         |\n",
            "|    time_elapsed         | 1363         |\n",
            "|    total_timesteps      | 369408       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.242205e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0464      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.395       |\n",
            "|    n_updates            | 106980       |\n",
            "|    policy_gradient_loss | -1.22e-05    |\n",
            "|    value_loss           | 0.857        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2887          |\n",
            "|    time_elapsed         | 1364          |\n",
            "|    total_timesteps      | 369536        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0081567e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0899       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.626        |\n",
            "|    n_updates            | 106990        |\n",
            "|    policy_gradient_loss | -0.000306     |\n",
            "|    value_loss           | 0.328         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2888          |\n",
            "|    time_elapsed         | 1364          |\n",
            "|    total_timesteps      | 369664        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.9566698e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.155         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.624        |\n",
            "|    n_updates            | 107000        |\n",
            "|    policy_gradient_loss | -0.000307     |\n",
            "|    value_loss           | 0.302         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2889         |\n",
            "|    time_elapsed         | 1365         |\n",
            "|    total_timesteps      | 369792       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.985866e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.113        |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.709       |\n",
            "|    n_updates            | 107010       |\n",
            "|    policy_gradient_loss | -0.000573    |\n",
            "|    value_loss           | 0.123        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2890         |\n",
            "|    time_elapsed         | 1365         |\n",
            "|    total_timesteps      | 369920       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 9.116251e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0214      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 0.117        |\n",
            "|    n_updates            | 107020       |\n",
            "|    policy_gradient_loss | -0.000204    |\n",
            "|    value_loss           | 1.78         |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2891         |\n",
            "|    time_elapsed         | 1366         |\n",
            "|    total_timesteps      | 370048       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 6.193062e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0919       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.308       |\n",
            "|    n_updates            | 107030       |\n",
            "|    policy_gradient_loss | -0.000401    |\n",
            "|    value_loss           | 0.95         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 259           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2892          |\n",
            "|    time_elapsed         | 1366          |\n",
            "|    total_timesteps      | 370176        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 0.00012016669 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.375         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.411        |\n",
            "|    n_updates            | 107040        |\n",
            "|    policy_gradient_loss | -0.000999     |\n",
            "|    value_loss           | 0.752         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 259          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2893         |\n",
            "|    time_elapsed         | 1366         |\n",
            "|    total_timesteps      | 370304       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 6.445218e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0827       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.478       |\n",
            "|    n_updates            | 107050       |\n",
            "|    policy_gradient_loss | 0.000145     |\n",
            "|    value_loss           | 0.594        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2894          |\n",
            "|    time_elapsed         | 1367          |\n",
            "|    total_timesteps      | 370432        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 9.9185854e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.171         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.813         |\n",
            "|    n_updates            | 107060        |\n",
            "|    policy_gradient_loss | 8.93e-06      |\n",
            "|    value_loss           | 3.32          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2895          |\n",
            "|    time_elapsed         | 1368          |\n",
            "|    total_timesteps      | 370560        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.5785918e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0622        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 2.99          |\n",
            "|    n_updates            | 107070        |\n",
            "|    policy_gradient_loss | -2.47e-05     |\n",
            "|    value_loss           | 7.88          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2896         |\n",
            "|    time_elapsed         | 1369         |\n",
            "|    total_timesteps      | 370688       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.356937e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.171       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.467       |\n",
            "|    n_updates            | 107080       |\n",
            "|    policy_gradient_loss | -9.13e-05    |\n",
            "|    value_loss           | 0.605        |\n",
            "------------------------------------------\n",
            "--------------------------------------------\n",
            "| rollout/                |                |\n",
            "|    ep_len_mean          | 7.41e+04       |\n",
            "|    ep_rew_mean          | 245            |\n",
            "| time/                   |                |\n",
            "|    fps                  | 270            |\n",
            "|    iterations           | 2897           |\n",
            "|    time_elapsed         | 1369           |\n",
            "|    total_timesteps      | 370816         |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 1.10692345e-05 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -1.1           |\n",
            "|    explained_variance   | -0.0221        |\n",
            "|    learning_rate        | 0.0003         |\n",
            "|    loss                 | -0.509         |\n",
            "|    n_updates            | 107090         |\n",
            "|    policy_gradient_loss | -0.000171      |\n",
            "|    value_loss           | 0.539          |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2898          |\n",
            "|    time_elapsed         | 1370          |\n",
            "|    total_timesteps      | 370944        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.5699305e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -1.31e-05     |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.648        |\n",
            "|    n_updates            | 107100        |\n",
            "|    policy_gradient_loss | -0.000173     |\n",
            "|    value_loss           | 0.255         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2899          |\n",
            "|    time_elapsed         | 1370          |\n",
            "|    total_timesteps      | 371072        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.9514544e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0249        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.164        |\n",
            "|    n_updates            | 107110        |\n",
            "|    policy_gradient_loss | -0.00015      |\n",
            "|    value_loss           | 1.24          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2900          |\n",
            "|    time_elapsed         | 1371          |\n",
            "|    total_timesteps      | 371200        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.3373868e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0462        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.262        |\n",
            "|    n_updates            | 107120        |\n",
            "|    policy_gradient_loss | -0.000226     |\n",
            "|    value_loss           | 1.05          |\n",
            "-------------------------------------------\n",
            "----------------------------------------\n",
            "| rollout/                |            |\n",
            "|    ep_len_mean          | 7.41e+04   |\n",
            "|    ep_rew_mean          | 245        |\n",
            "| time/                   |            |\n",
            "|    fps                  | 270        |\n",
            "|    iterations           | 2901       |\n",
            "|    time_elapsed         | 1371       |\n",
            "|    total_timesteps      | 371328     |\n",
            "| train/                  |            |\n",
            "|    approx_kl            | 8.1365e-06 |\n",
            "|    clip_fraction        | 0          |\n",
            "|    clip_range           | 0.2        |\n",
            "|    entropy_loss         | -1.1       |\n",
            "|    explained_variance   | -0.159     |\n",
            "|    learning_rate        | 0.0003     |\n",
            "|    loss                 | -0.6       |\n",
            "|    n_updates            | 107130     |\n",
            "|    policy_gradient_loss | -0.000317  |\n",
            "|    value_loss           | 0.367      |\n",
            "----------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2902          |\n",
            "|    time_elapsed         | 1371          |\n",
            "|    total_timesteps      | 371456        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1580996e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0542        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.625        |\n",
            "|    n_updates            | 107140        |\n",
            "|    policy_gradient_loss | -0.000111     |\n",
            "|    value_loss           | 0.312         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2903          |\n",
            "|    time_elapsed         | 1372          |\n",
            "|    total_timesteps      | 371584        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.4592428e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0476        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.125        |\n",
            "|    n_updates            | 107150        |\n",
            "|    policy_gradient_loss | 0.000318      |\n",
            "|    value_loss           | 1.4           |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2904          |\n",
            "|    time_elapsed         | 1372          |\n",
            "|    total_timesteps      | 371712        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.4368055e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.159         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.584        |\n",
            "|    n_updates            | 107160        |\n",
            "|    policy_gradient_loss | -6.91e-05     |\n",
            "|    value_loss           | 0.391         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2905         |\n",
            "|    time_elapsed         | 1373         |\n",
            "|    total_timesteps      | 371840       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.581401e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0822       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.591       |\n",
            "|    n_updates            | 107170       |\n",
            "|    policy_gradient_loss | -2.65e-05    |\n",
            "|    value_loss           | 0.369        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2906          |\n",
            "|    time_elapsed         | 1373          |\n",
            "|    total_timesteps      | 371968        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1777505e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00768      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.501        |\n",
            "|    n_updates            | 107180        |\n",
            "|    policy_gradient_loss | -0.000399     |\n",
            "|    value_loss           | 0.615         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2907         |\n",
            "|    time_elapsed         | 1374         |\n",
            "|    total_timesteps      | 372096       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.220063e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0196       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | 2            |\n",
            "|    n_updates            | 107190       |\n",
            "|    policy_gradient_loss | -0.000128    |\n",
            "|    value_loss           | 5.6          |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2908          |\n",
            "|    time_elapsed         | 1375          |\n",
            "|    total_timesteps      | 372224        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6319565e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0287       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.673        |\n",
            "|    n_updates            | 107200        |\n",
            "|    policy_gradient_loss | -0.000217     |\n",
            "|    value_loss           | 0.218         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2909          |\n",
            "|    time_elapsed         | 1375          |\n",
            "|    total_timesteps      | 372352        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6031397e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0616        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.253        |\n",
            "|    n_updates            | 107210        |\n",
            "|    policy_gradient_loss | -0.000447     |\n",
            "|    value_loss           | 1.1           |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2910          |\n",
            "|    time_elapsed         | 1376          |\n",
            "|    total_timesteps      | 372480        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.5099609e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.114        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.637        |\n",
            "|    n_updates            | 107220        |\n",
            "|    policy_gradient_loss | -0.000355     |\n",
            "|    value_loss           | 0.28          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2911          |\n",
            "|    time_elapsed         | 1376          |\n",
            "|    total_timesteps      | 372608        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1343975e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0134        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.368        |\n",
            "|    n_updates            | 107230        |\n",
            "|    policy_gradient_loss | 6.28e-05      |\n",
            "|    value_loss           | 0.867         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2912          |\n",
            "|    time_elapsed         | 1377          |\n",
            "|    total_timesteps      | 372736        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6805326e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.029         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.644        |\n",
            "|    n_updates            | 107240        |\n",
            "|    policy_gradient_loss | -0.000692     |\n",
            "|    value_loss           | 0.264         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2913          |\n",
            "|    time_elapsed         | 1377          |\n",
            "|    total_timesteps      | 372864        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2493768e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.091         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 5.63          |\n",
            "|    n_updates            | 107250        |\n",
            "|    policy_gradient_loss | -0.000255     |\n",
            "|    value_loss           | 13.1          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2914          |\n",
            "|    time_elapsed         | 1378          |\n",
            "|    total_timesteps      | 372992        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.4640391e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0413        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.385        |\n",
            "|    n_updates            | 107260        |\n",
            "|    policy_gradient_loss | 0.000166      |\n",
            "|    value_loss           | 0.777         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2915         |\n",
            "|    time_elapsed         | 1378         |\n",
            "|    total_timesteps      | 373120       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.306195e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0864      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.684       |\n",
            "|    n_updates            | 107270       |\n",
            "|    policy_gradient_loss | -6.03e-06    |\n",
            "|    value_loss           | 0.185        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2916          |\n",
            "|    time_elapsed         | 1379          |\n",
            "|    total_timesteps      | 373248        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.0368177e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.109         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.599        |\n",
            "|    n_updates            | 107280        |\n",
            "|    policy_gradient_loss | -3.39e-05     |\n",
            "|    value_loss           | 0.349         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2917          |\n",
            "|    time_elapsed         | 1379          |\n",
            "|    total_timesteps      | 373376        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.4947727e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.132         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.285         |\n",
            "|    n_updates            | 107290        |\n",
            "|    policy_gradient_loss | 9.88e-05      |\n",
            "|    value_loss           | 2.2           |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2918          |\n",
            "|    time_elapsed         | 1379          |\n",
            "|    total_timesteps      | 373504        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0570511e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0265       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.213         |\n",
            "|    n_updates            | 107300        |\n",
            "|    policy_gradient_loss | 1.3e-06       |\n",
            "|    value_loss           | 2.06          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2919          |\n",
            "|    time_elapsed         | 1380          |\n",
            "|    total_timesteps      | 373632        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3504177e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.105         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.903         |\n",
            "|    n_updates            | 107310        |\n",
            "|    policy_gradient_loss | 1.45e-06      |\n",
            "|    value_loss           | 3.43          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2920          |\n",
            "|    time_elapsed         | 1380          |\n",
            "|    total_timesteps      | 373760        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0803342e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.303        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.579        |\n",
            "|    n_updates            | 107320        |\n",
            "|    policy_gradient_loss | -4.19e-06     |\n",
            "|    value_loss           | 0.423         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2921         |\n",
            "|    time_elapsed         | 1381         |\n",
            "|    total_timesteps      | 373888       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.527369e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0645       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.575       |\n",
            "|    n_updates            | 107330       |\n",
            "|    policy_gradient_loss | -3.19e-06    |\n",
            "|    value_loss           | 0.391        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2922         |\n",
            "|    time_elapsed         | 1381         |\n",
            "|    total_timesteps      | 374016       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 7.278286e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0193       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.423       |\n",
            "|    n_updates            | 107340       |\n",
            "|    policy_gradient_loss | -8.46e-05    |\n",
            "|    value_loss           | 0.714        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2923          |\n",
            "|    time_elapsed         | 1382          |\n",
            "|    total_timesteps      | 374144        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.0719523e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00234      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.113         |\n",
            "|    n_updates            | 107350        |\n",
            "|    policy_gradient_loss | -4.77e-05     |\n",
            "|    value_loss           | 1.79          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2924          |\n",
            "|    time_elapsed         | 1382          |\n",
            "|    total_timesteps      | 374272        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.4416873e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0477       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.563        |\n",
            "|    n_updates            | 107360        |\n",
            "|    policy_gradient_loss | -4.29e-05     |\n",
            "|    value_loss           | 0.416         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2925          |\n",
            "|    time_elapsed         | 1382          |\n",
            "|    total_timesteps      | 374400        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.9455329e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0546        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.715        |\n",
            "|    n_updates            | 107370        |\n",
            "|    policy_gradient_loss | -7.4e-05      |\n",
            "|    value_loss           | 0.12          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2926          |\n",
            "|    time_elapsed         | 1383          |\n",
            "|    total_timesteps      | 374528        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.5571248e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0428        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.634        |\n",
            "|    n_updates            | 107380        |\n",
            "|    policy_gradient_loss | -0.000329     |\n",
            "|    value_loss           | 0.279         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2927          |\n",
            "|    time_elapsed         | 1383          |\n",
            "|    total_timesteps      | 374656        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.0761508e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0446        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.591        |\n",
            "|    n_updates            | 107390        |\n",
            "|    policy_gradient_loss | -0.00028      |\n",
            "|    value_loss           | 0.364         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2928          |\n",
            "|    time_elapsed         | 1384          |\n",
            "|    total_timesteps      | 374784        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 8.6729415e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0226       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.674        |\n",
            "|    n_updates            | 107400        |\n",
            "|    policy_gradient_loss | 3.19e-05      |\n",
            "|    value_loss           | 0.195         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2929          |\n",
            "|    time_elapsed         | 1384          |\n",
            "|    total_timesteps      | 374912        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1357479e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0451       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.381        |\n",
            "|    n_updates            | 107410        |\n",
            "|    policy_gradient_loss | 3.04e-05      |\n",
            "|    value_loss           | 0.79          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2930          |\n",
            "|    time_elapsed         | 1385          |\n",
            "|    total_timesteps      | 375040        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.7787872e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0913        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.612        |\n",
            "|    n_updates            | 107420        |\n",
            "|    policy_gradient_loss | -0.000378     |\n",
            "|    value_loss           | 0.317         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2931         |\n",
            "|    time_elapsed         | 1385         |\n",
            "|    total_timesteps      | 375168       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 4.828535e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0648       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.701       |\n",
            "|    n_updates            | 107430       |\n",
            "|    policy_gradient_loss | -0.000238    |\n",
            "|    value_loss           | 0.171        |\n",
            "------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 7.41e+04    |\n",
            "|    ep_rew_mean          | 245         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 270         |\n",
            "|    iterations           | 2932        |\n",
            "|    time_elapsed         | 1385        |\n",
            "|    total_timesteps      | 375296      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 8.81264e-06 |\n",
            "|    clip_fraction        | 0           |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.1        |\n",
            "|    explained_variance   | 0.000332    |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | -0.518      |\n",
            "|    n_updates            | 107440      |\n",
            "|    policy_gradient_loss | 4.39e-06    |\n",
            "|    value_loss           | 0.573       |\n",
            "-----------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2933         |\n",
            "|    time_elapsed         | 1386         |\n",
            "|    total_timesteps      | 375424       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.969964e-05 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0317      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.289       |\n",
            "|    n_updates            | 107450       |\n",
            "|    policy_gradient_loss | -0.000624    |\n",
            "|    value_loss           | 0.989        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2934          |\n",
            "|    time_elapsed         | 1386          |\n",
            "|    total_timesteps      | 375552        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.9291026e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0057       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.241        |\n",
            "|    n_updates            | 107460        |\n",
            "|    policy_gradient_loss | 0.00012       |\n",
            "|    value_loss           | 1.11          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2935          |\n",
            "|    time_elapsed         | 1387          |\n",
            "|    total_timesteps      | 375680        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.9841827e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0641        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.0389        |\n",
            "|    n_updates            | 107470        |\n",
            "|    policy_gradient_loss | 1.58e-05      |\n",
            "|    value_loss           | 1.63          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2936          |\n",
            "|    time_elapsed         | 1388          |\n",
            "|    total_timesteps      | 375808        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.0414743e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0276        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.637        |\n",
            "|    n_updates            | 107480        |\n",
            "|    policy_gradient_loss | -0.000117     |\n",
            "|    value_loss           | 0.275         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2937          |\n",
            "|    time_elapsed         | 1388          |\n",
            "|    total_timesteps      | 375936        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.9201924e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0789       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.399        |\n",
            "|    n_updates            | 107490        |\n",
            "|    policy_gradient_loss | -4.31e-05     |\n",
            "|    value_loss           | 0.78          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2938         |\n",
            "|    time_elapsed         | 1389         |\n",
            "|    total_timesteps      | 376064       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 2.563931e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0562       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.544       |\n",
            "|    n_updates            | 107500       |\n",
            "|    policy_gradient_loss | -2.9e-05     |\n",
            "|    value_loss           | 0.486        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2939          |\n",
            "|    time_elapsed         | 1389          |\n",
            "|    total_timesteps      | 376192        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6763806e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0968       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.69         |\n",
            "|    n_updates            | 107510        |\n",
            "|    policy_gradient_loss | -4.82e-05     |\n",
            "|    value_loss           | 0.162         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2940          |\n",
            "|    time_elapsed         | 1390          |\n",
            "|    total_timesteps      | 376320        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.0757783e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.104         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.644        |\n",
            "|    n_updates            | 107520        |\n",
            "|    policy_gradient_loss | -0.000446     |\n",
            "|    value_loss           | 0.271         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2941          |\n",
            "|    time_elapsed         | 1390          |\n",
            "|    total_timesteps      | 376448        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2634398e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0715        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.123        |\n",
            "|    n_updates            | 107530        |\n",
            "|    policy_gradient_loss | 0.000551      |\n",
            "|    value_loss           | 1.3           |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2942          |\n",
            "|    time_elapsed         | 1391          |\n",
            "|    total_timesteps      | 376576        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.8470683e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.114        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.547        |\n",
            "|    n_updates            | 107540        |\n",
            "|    policy_gradient_loss | -8.31e-05     |\n",
            "|    value_loss           | 0.474         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2943          |\n",
            "|    time_elapsed         | 1391          |\n",
            "|    total_timesteps      | 376704        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.3107044e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.187         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.201        |\n",
            "|    n_updates            | 107550        |\n",
            "|    policy_gradient_loss | -0.000375     |\n",
            "|    value_loss           | 1.19          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2944         |\n",
            "|    time_elapsed         | 1392         |\n",
            "|    total_timesteps      | 376832       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.675014e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.105       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.469       |\n",
            "|    n_updates            | 107560       |\n",
            "|    policy_gradient_loss | -4.23e-05    |\n",
            "|    value_loss           | 0.614        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2945          |\n",
            "|    time_elapsed         | 1392          |\n",
            "|    total_timesteps      | 376960        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3113022e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0141        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.374        |\n",
            "|    n_updates            | 107570        |\n",
            "|    policy_gradient_loss | 8.78e-05      |\n",
            "|    value_loss           | 0.824         |\n",
            "-------------------------------------------\n",
            "--------------------------------------------\n",
            "| rollout/                |                |\n",
            "|    ep_len_mean          | 7.41e+04       |\n",
            "|    ep_rew_mean          | 245            |\n",
            "| time/                   |                |\n",
            "|    fps                  | 270            |\n",
            "|    iterations           | 2946           |\n",
            "|    time_elapsed         | 1393           |\n",
            "|    total_timesteps      | 377088         |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 1.21071935e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -1.1           |\n",
            "|    explained_variance   | 0.0123         |\n",
            "|    learning_rate        | 0.0003         |\n",
            "|    loss                 | -0.523         |\n",
            "|    n_updates            | 107580         |\n",
            "|    policy_gradient_loss | 5.12e-07       |\n",
            "|    value_loss           | 0.5            |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2947          |\n",
            "|    time_elapsed         | 1393          |\n",
            "|    total_timesteps      | 377216        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1967495e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00343      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.301        |\n",
            "|    n_updates            | 107590        |\n",
            "|    policy_gradient_loss | -1.29e-05     |\n",
            "|    value_loss           | 0.962         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2948          |\n",
            "|    time_elapsed         | 1394          |\n",
            "|    total_timesteps      | 377344        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.3481047e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.104         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.435        |\n",
            "|    n_updates            | 107600        |\n",
            "|    policy_gradient_loss | -1.91e-05     |\n",
            "|    value_loss           | 0.689         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2949          |\n",
            "|    time_elapsed         | 1394          |\n",
            "|    total_timesteps      | 377472        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2458844e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.058         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.27         |\n",
            "|    n_updates            | 107610        |\n",
            "|    policy_gradient_loss | -0.000155     |\n",
            "|    value_loss           | 1.02          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2950          |\n",
            "|    time_elapsed         | 1394          |\n",
            "|    total_timesteps      | 377600        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.4230761e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.065         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.558        |\n",
            "|    n_updates            | 107620        |\n",
            "|    policy_gradient_loss | -9.43e-05     |\n",
            "|    value_loss           | 0.48          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2951          |\n",
            "|    time_elapsed         | 1395          |\n",
            "|    total_timesteps      | 377728        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.9196963e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00654      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.472         |\n",
            "|    n_updates            | 107630        |\n",
            "|    policy_gradient_loss | -1.65e-05     |\n",
            "|    value_loss           | 2.49          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2952          |\n",
            "|    time_elapsed         | 1395          |\n",
            "|    total_timesteps      | 377856        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.4416873e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00283       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.432         |\n",
            "|    n_updates            | 107640        |\n",
            "|    policy_gradient_loss | -8.77e-05     |\n",
            "|    value_loss           | 2.52          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2953          |\n",
            "|    time_elapsed         | 1396          |\n",
            "|    total_timesteps      | 377984        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 7.1316026e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00742      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.509        |\n",
            "|    n_updates            | 107650        |\n",
            "|    policy_gradient_loss | -0.000179     |\n",
            "|    value_loss           | 0.545         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2954          |\n",
            "|    time_elapsed         | 1396          |\n",
            "|    total_timesteps      | 378112        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.1928921e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00168      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.427        |\n",
            "|    n_updates            | 107660        |\n",
            "|    policy_gradient_loss | -0.000258     |\n",
            "|    value_loss           | 0.688         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2955         |\n",
            "|    time_elapsed         | 1397         |\n",
            "|    total_timesteps      | 378240       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 6.907154e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.00208     |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.554       |\n",
            "|    n_updates            | 107670       |\n",
            "|    policy_gradient_loss | -7.61e-05    |\n",
            "|    value_loss           | 0.475        |\n",
            "------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2956         |\n",
            "|    time_elapsed         | 1397         |\n",
            "|    total_timesteps      | 378368       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.811453e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0164       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.107       |\n",
            "|    n_updates            | 107680       |\n",
            "|    policy_gradient_loss | 3.47e-05     |\n",
            "|    value_loss           | 1.37         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2957          |\n",
            "|    time_elapsed         | 1397          |\n",
            "|    total_timesteps      | 378496        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.5739656e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.000228     |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.587        |\n",
            "|    n_updates            | 107690        |\n",
            "|    policy_gradient_loss | -3.35e-05     |\n",
            "|    value_loss           | 0.365         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2958         |\n",
            "|    time_elapsed         | 1398         |\n",
            "|    total_timesteps      | 378624       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 8.557923e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.00879     |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.582       |\n",
            "|    n_updates            | 107700       |\n",
            "|    policy_gradient_loss | -0.000288    |\n",
            "|    value_loss           | 0.383        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2959          |\n",
            "|    time_elapsed         | 1398          |\n",
            "|    total_timesteps      | 378752        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 9.9982135e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.213         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.583        |\n",
            "|    n_updates            | 107710        |\n",
            "|    policy_gradient_loss | 9.64e-05      |\n",
            "|    value_loss           | 0.386         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2960          |\n",
            "|    time_elapsed         | 1399          |\n",
            "|    total_timesteps      | 378880        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8952414e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0498        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.184        |\n",
            "|    n_updates            | 107720        |\n",
            "|    policy_gradient_loss | -2.72e-05     |\n",
            "|    value_loss           | 1.28          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2961          |\n",
            "|    time_elapsed         | 1399          |\n",
            "|    total_timesteps      | 379008        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.2687873e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0794        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.496        |\n",
            "|    n_updates            | 107730        |\n",
            "|    policy_gradient_loss | -0.000283     |\n",
            "|    value_loss           | 0.554         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2962         |\n",
            "|    time_elapsed         | 1400         |\n",
            "|    total_timesteps      | 379136       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 6.253831e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0178      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.31        |\n",
            "|    n_updates            | 107740       |\n",
            "|    policy_gradient_loss | 0.000375     |\n",
            "|    value_loss           | 0.96         |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2963          |\n",
            "|    time_elapsed         | 1400          |\n",
            "|    total_timesteps      | 379264        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.6379864e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0019        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.282        |\n",
            "|    n_updates            | 107750        |\n",
            "|    policy_gradient_loss | -5.58e-05     |\n",
            "|    value_loss           | 0.994         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2964          |\n",
            "|    time_elapsed         | 1401          |\n",
            "|    total_timesteps      | 379392        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.7171488e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0509        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.146        |\n",
            "|    n_updates            | 107760        |\n",
            "|    policy_gradient_loss | -7.49e-05     |\n",
            "|    value_loss           | 1.36          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2965          |\n",
            "|    time_elapsed         | 1401          |\n",
            "|    total_timesteps      | 379520        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.9958243e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.0279       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.252         |\n",
            "|    n_updates            | 107770        |\n",
            "|    policy_gradient_loss | -0.000185     |\n",
            "|    value_loss           | 2.06          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2966          |\n",
            "|    time_elapsed         | 1402          |\n",
            "|    total_timesteps      | 379648        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.1431985e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0277        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.325         |\n",
            "|    n_updates            | 107780        |\n",
            "|    policy_gradient_loss | 5.85e-05      |\n",
            "|    value_loss           | 2.26          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2967          |\n",
            "|    time_elapsed         | 1402          |\n",
            "|    total_timesteps      | 379776        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.2200326e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0531        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.132        |\n",
            "|    n_updates            | 107790        |\n",
            "|    policy_gradient_loss | -1.19e-05     |\n",
            "|    value_loss           | 1.29          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2968          |\n",
            "|    time_elapsed         | 1403          |\n",
            "|    total_timesteps      | 379904        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.1886385e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00582       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.553        |\n",
            "|    n_updates            | 107800        |\n",
            "|    policy_gradient_loss | -2.97e-05     |\n",
            "|    value_loss           | 0.468         |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2969         |\n",
            "|    time_elapsed         | 1404         |\n",
            "|    total_timesteps      | 380032       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 3.207475e-06 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | 0.0832       |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.608       |\n",
            "|    n_updates            | 107810       |\n",
            "|    policy_gradient_loss | -0.000131    |\n",
            "|    value_loss           | 0.326        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2970          |\n",
            "|    time_elapsed         | 1404          |\n",
            "|    total_timesteps      | 380160        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.7907233e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | -0.00577      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.558        |\n",
            "|    n_updates            | 107820        |\n",
            "|    policy_gradient_loss | -5.42e-05     |\n",
            "|    value_loss           | 0.434         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2971          |\n",
            "|    time_elapsed         | 1404          |\n",
            "|    total_timesteps      | 380288        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8100254e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0491        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.424        |\n",
            "|    n_updates            | 107830        |\n",
            "|    policy_gradient_loss | -1.93e-05     |\n",
            "|    value_loss           | 0.756         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2972          |\n",
            "|    time_elapsed         | 1405          |\n",
            "|    total_timesteps      | 380416        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.7415732e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00688       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.55         |\n",
            "|    n_updates            | 107840        |\n",
            "|    policy_gradient_loss | -9.85e-05     |\n",
            "|    value_loss           | 0.442         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2973          |\n",
            "|    time_elapsed         | 1405          |\n",
            "|    total_timesteps      | 380544        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6612543e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.188         |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.213        |\n",
            "|    n_updates            | 107850        |\n",
            "|    policy_gradient_loss | -0.000191     |\n",
            "|    value_loss           | 1.16          |\n",
            "-------------------------------------------\n",
            "------------------------------------------\n",
            "| rollout/                |              |\n",
            "|    ep_len_mean          | 7.41e+04     |\n",
            "|    ep_rew_mean          | 245          |\n",
            "| time/                   |              |\n",
            "|    fps                  | 270          |\n",
            "|    iterations           | 2974         |\n",
            "|    time_elapsed         | 1406         |\n",
            "|    total_timesteps      | 380672       |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.820766e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -1.1         |\n",
            "|    explained_variance   | -0.0102      |\n",
            "|    learning_rate        | 0.0003       |\n",
            "|    loss                 | -0.504       |\n",
            "|    n_updates            | 107860       |\n",
            "|    policy_gradient_loss | -6.07e-06    |\n",
            "|    value_loss           | 0.568        |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2975          |\n",
            "|    time_elapsed         | 1406          |\n",
            "|    total_timesteps      | 380800        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.7601997e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0024        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.697        |\n",
            "|    n_updates            | 107870        |\n",
            "|    policy_gradient_loss | 8.66e-06      |\n",
            "|    value_loss           | 0.145         |\n",
            "-------------------------------------------\n",
            "-----------------------------------------\n",
            "| rollout/                |             |\n",
            "|    ep_len_mean          | 7.41e+04    |\n",
            "|    ep_rew_mean          | 245         |\n",
            "| time/                   |             |\n",
            "|    fps                  | 270         |\n",
            "|    iterations           | 2976        |\n",
            "|    time_elapsed         | 1407        |\n",
            "|    total_timesteps      | 380928      |\n",
            "| train/                  |             |\n",
            "|    approx_kl            | 1.96537e-05 |\n",
            "|    clip_fraction        | 0           |\n",
            "|    clip_range           | 0.2         |\n",
            "|    entropy_loss         | -1.1        |\n",
            "|    explained_variance   | -0.0307     |\n",
            "|    learning_rate        | 0.0003      |\n",
            "|    loss                 | -0.699      |\n",
            "|    n_updates            | 107880      |\n",
            "|    policy_gradient_loss | -0.00039    |\n",
            "|    value_loss           | 0.154       |\n",
            "-----------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2977          |\n",
            "|    time_elapsed         | 1407          |\n",
            "|    total_timesteps      | 381056        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.5780558e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.000162      |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.671        |\n",
            "|    n_updates            | 107890        |\n",
            "|    policy_gradient_loss | -0.00058      |\n",
            "|    value_loss           | 0.202         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2978          |\n",
            "|    time_elapsed         | 1408          |\n",
            "|    total_timesteps      | 381184        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.7035905e-05 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.00496       |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | 0.239         |\n",
            "|    n_updates            | 107900        |\n",
            "|    policy_gradient_loss | 2.61e-09      |\n",
            "|    value_loss           | 2.03          |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2979          |\n",
            "|    time_elapsed         | 1408          |\n",
            "|    total_timesteps      | 381312        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 5.7294965e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.0845        |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.418        |\n",
            "|    n_updates            | 107910        |\n",
            "|    policy_gradient_loss | -0.000332     |\n",
            "|    value_loss           | 0.748         |\n",
            "-------------------------------------------\n",
            "-------------------------------------------\n",
            "| rollout/                |               |\n",
            "|    ep_len_mean          | 7.41e+04      |\n",
            "|    ep_rew_mean          | 245           |\n",
            "| time/                   |               |\n",
            "|    fps                  | 270           |\n",
            "|    iterations           | 2980          |\n",
            "|    time_elapsed         | 1408          |\n",
            "|    total_timesteps      | 381440        |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.2945384e-06 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -1.1          |\n",
            "|    explained_variance   | 0.2           |\n",
            "|    learning_rate        | 0.0003        |\n",
            "|    loss                 | -0.239        |\n",
            "|    n_updates            | 107920        |\n",
            "|    policy_gradient_loss | 7.91e-05      |\n",
            "|    value_loss           | 1.08          |\n",
            "-------------------------------------------\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-608-f54bd1998566>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#model = RecurrentPPO('MlpLstmPolicy', env, verbose=1, ent_coef=0.60, device='cuda')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sb3_contrib/ppo_recurrent/ppo_recurrent.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m    467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m             \u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_rollouts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrollout_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_rollout_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcontinue_training\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sb3_contrib/ppo_recurrent/ppo_recurrent.py\u001b[0m in \u001b[0;36mcollect_rollouts\u001b[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001b[0m\n\u001b[1;32m    242\u001b[0m                 \u001b[0mobs_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobs_as_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m                 \u001b[0mepisode_starts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_last_episode_starts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m                 \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_probs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlstm_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolicy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobs_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlstm_states\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepisode_starts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[0mactions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sb3_contrib/common/recurrent/policies.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, obs, lstm_states, episode_starts, deterministic)\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlatent_vf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mdistribution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_action_dist_from_latent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlatent_pi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0mactions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_actions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeterministic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeterministic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    256\u001b[0m         \u001b[0mlog_prob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_prob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRNNStates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_states_pi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlstm_states_vf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/stable_baselines3/common/distributions.py\u001b[0m in \u001b[0;36mget_actions\u001b[0;34m(self, deterministic)\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeterministic\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/stable_baselines3/common/distributions.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/distributions/categorical.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, sample_shape)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0msample_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0mprobs_2d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_events\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0msamples_2d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultinomial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprobs_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msamples_2d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extended_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env = TradingEnv(cleaned_df)\n",
        "\n",
        "observation = env.reset()\n",
        "\n",
        "observation_arr = []\n",
        "action_arr = []\n",
        "\n",
        "for i in range(50):\n",
        "    action, _ = model.predict(observation)\n",
        "    obs, rewards, done, _ = env.step(action)\n",
        "    observation_arr.append(obs)\n",
        "    action_arr.append(action)\n",
        "    print({\"Rewards\": rewards, \"action\": action})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ryjy7V-c9Via",
        "outputId": "2fabe4c7-00cf-40a5-e543-9b265343df47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': -0.9798111333950033, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.3047480323893126, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.33812235230996635, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 5.941832929620006, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.2943331597101917, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': -0.22885093187348104, 'action': array(1)}\n",
            "{'Rewards': 0.0, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': -0.1962702274944661, 'action': array(1)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.0, 'action': array(0)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 0.001, 'action': array(2)}\n",
            "{'Rewards': 9.183793562278098, 'action': array(1)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine observations into a single array\n",
        "combined_observations = np.array(observation_arr)\n",
        "\n",
        "# Create figure\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot price movements (observation index 0)\n",
        "plt.plot(combined_observations[:, 0], linewidth=2) #label='Price Movements', linewidth=2)\n",
        "\n",
        "# Plot action dots\n",
        "for t, action in enumerate(action_arr):\n",
        "    if action == 0:  # Buy\n",
        "        plt.scatter(t, combined_observations[t, 0], color='green', s=100)\n",
        "    elif action == 1:  # Sell\n",
        "        plt.scatter(t, combined_observations[t, 0], color='red', s=100)\n",
        "    elif action == 2:  # Hold\n",
        "        plt.scatter(t, combined_observations[t, 0], color='blue', s=100)\n",
        "\n",
        "plt.xlabel('Time step')\n",
        "plt.ylabel('Price')\n",
        "plt.title('Price Movements with Actions')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "R2QlDsuaXXyI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "5ec99647-3d31-4b3e-b1d0-12903f75bcdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env.render()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGsd1o-hkH43",
        "outputId": "03f40f6e-a6f6-4f15-d817-e38497c39111"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock Price: 320.8499999999999\n",
            "Account Balance: 5380.210045281541\n",
            "Number of Shares: 0\n",
            "Profit: 1.0750419938479097\n",
            "--------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bq-Z3mc4sTBI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}