ppo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from tensordict.nn import TensorDictModule\n",
    "from tensordict.nn.distributions import NormalParamExtractor\n",
    "from torch import nn\n",
    "from torchrl.collectors import SyncDataCollector\n",
    "from torchrl.data.replay_buffers import ReplayBuffer\n",
    "from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement\n",
    "from torchrl.data.replay_buffers.storages import LazyTensorStorage\n",
    "from torchrl.envs import (\n",
    "    Compose,\n",
    "    DoubleToFloat,\n",
    "    ObservationNorm,\n",
    "    StepCounter,\n",
    "    ToTensorImage,\n",
    "    TransformedEnv,\n",
    ")\n",
    "from torchrl.envs.libs.gym import GymEnv\n",
    "from torchrl.envs.utils import check_env_specs, ExplorationType, set_exploration_type\n",
    "from torchrl.modules import ProbabilisticActor, TanhNormal, ValueOperator\n",
    "from torchrl.objectives import ClipPPOLoss\n",
    "from torchrl.objectives.value import GAE\n",
    "from tqdm import tqdm\n",
    "\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "num_cells = 256  # number of cells in each layer\n",
    "lr = 3e-4\n",
    "max_grad_norm = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_skip = 1\n",
    "frames_per_batch = 1000 // frame_skip\n",
    "total_frames = 10_000 // frame_skip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_batch_size = 64  # cardinality of the sub-samples gathered from the current data in the inner loop\n",
    "num_epochs = 10  # optimisation steps per batch of data collected\n",
    "clip_epsilon = (\n",
    "    0.2  # clip value for PPO loss: see the equation in the intro for more context.\n",
    ")\n",
    "gamma = 0.99\n",
    "lmbda = 0.95\n",
    "entropy_eps = 1e-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_env = GymEnv(\"CarRacing-v2\", device=device, frame_skip=frame_skip)\n",
    "\n",
    "base_env = TransformedEnv(\n",
    "    base_env,\n",
    "    Compose(\n",
    "        DoubleToFloat(in_keys=[\"pixels\"]),\n",
    "        ToTensorImage(in_keys=[\"pixels\"]),\n",
    "        StepCounter(),\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation spec: CompositeSpec(\n",
      "    pixels: BoundedTensorSpec(\n",
      "         shape=torch.Size([3, 96, 96]), space=ContinuousBox(minimum=Tensor(shape=torch.Size([3, 96, 96]), device=cpu, dtype=torch.float32, contiguous=False), maximum=Tensor(shape=torch.Size([3, 96, 96]), device=cpu, dtype=torch.float32, contiguous=False)), device=cpu, dtype=torch.float32, domain=continuous),\n",
      "    step_count: UnboundedDiscreteTensorSpec(\n",
      "         shape=torch.Size([1]), space=ContinuousBox(minimum=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.int64, contiguous=True), maximum=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.int64, contiguous=True)), device=cpu, dtype=torch.int64, domain=continuous), device=cpu, shape=torch.Size([]))\n",
      "Action spec: BoundedTensorSpec(\n",
      "     shape=torch.Size([3]), space=ContinuousBox(minimum=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True), maximum=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous)\n",
      "Reward spec: UnboundedContinuousTensorSpec(\n",
      "     shape=torch.Size([1]), space=ContinuousBox(minimum=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), maximum=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous)\n",
      "Input spec: CompositeSpec(\n",
      "    action: BoundedTensorSpec(\n",
      "         shape=torch.Size([3]), space=ContinuousBox(minimum=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True), maximum=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous),\n",
      "    step_count: UnboundedDiscreteTensorSpec(\n",
      "         shape=torch.Size([1]), space=ContinuousBox(minimum=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.int64, contiguous=True), maximum=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.int64, contiguous=True)), device=cpu, dtype=torch.int64, domain=continuous), device=cpu, shape=torch.Size([]))\n"
     ]
    }
   ],
   "source": [
    "print(\"Observation spec:\", base_env.observation_spec)\n",
    "print(\"Action spec:\", base_env.action_spec)\n",
    "print(\"Reward spec:\", base_env.reward_spec)\n",
    "print(\"Input spec:\", base_env.input_spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rollout spec: TensorDict(\n",
      "    fields={\n",
      "        action: Tensor(shape=torch.Size([1, 3]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        done: Tensor(shape=torch.Size([1, 1]), device=cpu, dtype=torch.bool, is_shared=False),\n",
      "        next: TensorDict(\n",
      "            fields={\n",
      "                done: Tensor(shape=torch.Size([1, 1]), device=cpu, dtype=torch.bool, is_shared=False),\n",
      "                pixels: Tensor(shape=torch.Size([1, 3, 96, 96]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "                reward: Tensor(shape=torch.Size([1, 1]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "                step_count: Tensor(shape=torch.Size([1, 1]), device=cpu, dtype=torch.int64, is_shared=False)},\n",
      "            batch_size=torch.Size([1]),\n",
      "            device=cpu,\n",
      "            is_shared=False),\n",
      "        pixels: Tensor(shape=torch.Size([1, 3, 96, 96]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        step_count: Tensor(shape=torch.Size([1, 1]), device=cpu, dtype=torch.int64, is_shared=False)},\n",
      "    batch_size=torch.Size([1]),\n",
      "    device=cpu,\n",
      "    is_shared=False)\n",
      "Shape of the rollout: torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "rollout = base_env.rollout(1)\n",
    "print(\"Rollout spec:\", rollout)\n",
    "print(\"Shape of the rollout:\", rollout.batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1            [-1, 1, 47, 47]              49\n",
      "           Flatten-2                 [-1, 2209]               0\n",
      "            Linear-3                  [-1, 256]         565,760\n",
      "              Tanh-4                  [-1, 256]               0\n",
      "            Linear-5                  [-1, 256]          65,792\n",
      "              Tanh-6                  [-1, 256]               0\n",
      "            Linear-7                  [-1, 256]          65,792\n",
      "              Tanh-8                  [-1, 256]               0\n",
      "            Linear-9                    [-1, 6]           1,542\n",
      "NormalParamExtractor-10         [[-1, 3], [-1, 3]]               0\n",
      "================================================================\n",
      "Total params: 698,935\n",
      "Trainable params: 698,935\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.11\n",
      "Forward/backward pass size (MB): 0.05\n",
      "Params size (MB): 2.67\n",
      "Estimated Total Size (MB): 2.82\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "actor_net = nn.Sequential(\n",
    "    nn.Conv2d(3, 1, kernel_size=4, stride=2, device=device),\n",
    "    nn.Flatten(),\n",
    "    nn.LazyLinear(num_cells, device=device),\n",
    "    nn.Tanh(),\n",
    "    nn.LazyLinear(num_cells, device=device),\n",
    "    nn.Tanh(),\n",
    "    nn.LazyLinear(num_cells, device=device),\n",
    "    nn.Tanh(),\n",
    "    nn.LazyLinear(2 * base_env.action_spec.shape[-1], device=device),\n",
    "    NormalParamExtractor(),\n",
    ")\n",
    "\n",
    "policy_module = TensorDictModule(\n",
    "    actor_net, in_keys=[\"pixels\"], out_keys=[\"loc\", \"scale\"]\n",
    ")\n",
    "\n",
    "policy_module = ProbabilisticActor(\n",
    "    module=policy_module,\n",
    "    spec=base_env.action_spec,\n",
    "    in_keys=[\"loc\", \"scale\"],\n",
    "    distribution_class=TanhNormal,\n",
    "    distribution_kwargs={\n",
    "        \"min\": base_env.action_spec.space.minimum,\n",
    "        \"max\": base_env.action_spec.space.maximum,\n",
    "    },\n",
    "    return_log_prob=True,\n",
    ")\n",
    "\n",
    "summary(actor_net, input_size=(3,96,96))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1            [-1, 1, 47, 47]              49\n",
      "           Flatten-2                 [-1, 2209]               0\n",
      "            Linear-3                  [-1, 256]         565,760\n",
      "              Tanh-4                  [-1, 256]               0\n",
      "            Linear-5                  [-1, 256]          65,792\n",
      "              Tanh-6                  [-1, 256]               0\n",
      "            Linear-7                  [-1, 256]          65,792\n",
      "              Tanh-8                  [-1, 256]               0\n",
      "            Linear-9                    [-1, 1]             257\n",
      "================================================================\n",
      "Total params: 697,650\n",
      "Trainable params: 697,650\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.11\n",
      "Forward/backward pass size (MB): 0.05\n",
      "Params size (MB): 2.66\n",
      "Estimated Total Size (MB): 2.81\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "value_network = nn.Sequential(\n",
    "    nn.Conv2d(3, 1, kernel_size=4, stride=2, device=device),\n",
    "    nn.Flatten(),\n",
    "    nn.LazyLinear(num_cells, device=device),\n",
    "    nn.Tanh(),\n",
    "    nn.LazyLinear(num_cells, device=device),\n",
    "    nn.Tanh(),\n",
    "    nn.LazyLinear(num_cells, device=device),\n",
    "    nn.Tanh(),\n",
    "    nn.LazyLinear(1, device=device),\n",
    ")\n",
    "\n",
    "value_module = ValueOperator(value_network, in_keys=[\"pixels\"])\n",
    "\n",
    "summary(value_network, input_size=(3,96,96,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running policy: TensorDict(\n",
      "    fields={\n",
      "        action: Tensor(shape=torch.Size([1, 3]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),\n",
      "        loc: Tensor(shape=torch.Size([1, 3]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        pixels: Tensor(shape=torch.Size([3, 96, 96]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        sample_log_prob: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        scale: Tensor(shape=torch.Size([1, 3]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        step_count: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.int64, is_shared=False)},\n",
      "    batch_size=torch.Size([]),\n",
      "    device=cpu,\n",
      "    is_shared=False)\n",
      "Running value: TensorDict(\n",
      "    fields={\n",
      "        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),\n",
      "        pixels: Tensor(shape=torch.Size([3, 96, 96]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        state_value: Tensor(shape=torch.Size([1, 1]), device=cpu, dtype=torch.float32, is_shared=False),\n",
      "        step_count: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.int64, is_shared=False)},\n",
      "    batch_size=torch.Size([]),\n",
      "    device=cpu,\n",
      "    is_shared=False)\n"
     ]
    }
   ],
   "source": [
    "print(\"Running policy:\", policy_module(base_env.reset()))\n",
    "print(\"Running value:\", value_module(base_env.reset()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "collector = SyncDataCollector(\n",
    "    base_env,\n",
    "    policy_module,\n",
    "    frames_per_batch=frames_per_batch,\n",
    "    total_frames=total_frames,\n",
    "    split_trajs=False,\n",
    "    device=device,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "replay_buffer = ReplayBuffer(\n",
    "    storage=LazyTensorStorage(frames_per_batch),\n",
    "    sampler=SamplerWithoutReplacement(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "advantage_module = GAE(gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True)\n",
    "\n",
    "loss_module = ClipPPOLoss(\n",
    "    actor=policy_module,\n",
    "    critic=value_module,\n",
    "    advantage_key=\"advantage\",\n",
    "    clip_epsilon=clip_epsilon,\n",
    "    entropy_bonus=bool(entropy_eps),\n",
    "    entropy_coef=entropy_eps,\n",
    "    value_target_key=advantage_module.value_target_key,\n",
    "    critic_coef=1.0,\n",
    "    gamma=0.99,\n",
    "    loss_critic_type=\"smooth_l1\",\n",
    ")\n",
    "\n",
    "optim = torch.optim.Adam(loss_module.parameters(), lr)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, total_frames // frames_per_batch, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10000 [01:20<?, ?it/s]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [39], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m pbar \u001b[39m=\u001b[39m tqdm(total\u001b[39m=\u001b[39mtotal_frames \u001b[39m*\u001b[39m frame_skip)\n\u001b[1;32m      3\u001b[0m eval_str \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[39mfor\u001b[39;00m i, tensordict_data \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(collector):\n\u001b[1;32m      6\u001b[0m     \u001b[39mfor\u001b[39;00m _ \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_epochs):\n\u001b[1;32m      7\u001b[0m         \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/torchrl/collectors/collectors.py:731\u001b[0m, in \u001b[0;36mSyncDataCollector.iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    729\u001b[0m i \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    730\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iter \u001b[39m=\u001b[39m i\n\u001b[0;32m--> 731\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrollout())\n\u001b[1;32m    732\u001b[0m tensordict_out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrollout()\n\u001b[1;32m    733\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_frames \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m tensordict_out\u001b[39m.\u001b[39mnumel()\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/torchrl/_utils.py:361\u001b[0m, in \u001b[0;36maccept_remote_rref_invocation.<locals>.unpack_rref_and_invoke_function\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m _os_is_windows \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m, torch\u001b[39m.\u001b[39m_C\u001b[39m.\u001b[39m_distributed_rpc\u001b[39m.\u001b[39mPyRRef):\n\u001b[1;32m    360\u001b[0m     \u001b[39mself\u001b[39m \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlocal_value()\n\u001b[0;32m--> 361\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[39mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/torchrl/collectors/collectors.py:827\u001b[0m, in \u001b[0;36mSyncDataCollector.rollout\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    825\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    826\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpolicy(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tensordict)\n\u001b[0;32m--> 827\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menv\u001b[39m.\u001b[39;49mstep(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_tensordict)\n\u001b[1;32m    829\u001b[0m \u001b[39m# we must clone all the values, since the step / traj_id updates are done in-place\u001b[39;00m\n\u001b[1;32m    830\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/torchrl/envs/common.py:378\u001b[0m, in \u001b[0;36mEnvBase.step\u001b[0;34m(self, tensordict)\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_assert_tensordict_shape(tensordict)\n\u001b[1;32m    377\u001b[0m tensordict\u001b[39m.\u001b[39mlock_()  \u001b[39m# make sure _step does not modify the tensordict\u001b[39;00m\n\u001b[0;32m--> 378\u001b[0m tensordict_out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_step(tensordict)\n\u001b[1;32m    379\u001b[0m \u001b[39m# this tensordict should contain a \"next\" key\u001b[39;00m\n\u001b[1;32m    380\u001b[0m next_tensordict_out \u001b[39m=\u001b[39m tensordict_out\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnext\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/torchrl/envs/transforms/transforms.py:583\u001b[0m, in \u001b[0;36mTransformedEnv._step\u001b[0;34m(self, tensordict)\u001b[0m\n\u001b[1;32m    581\u001b[0m tensordict \u001b[39m=\u001b[39m tensordict\u001b[39m.\u001b[39mclone(\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m    582\u001b[0m tensordict_in \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform\u001b[39m.\u001b[39minv(tensordict)\n\u001b[0;32m--> 583\u001b[0m tensordict_out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbase_env\u001b[39m.\u001b[39;49m_step(tensordict_in)\n\u001b[1;32m    584\u001b[0m \u001b[39m# we want the input entries to remain unchanged\u001b[39;00m\n\u001b[1;32m    585\u001b[0m tensordict_out \u001b[39m=\u001b[39m tensordict\u001b[39m.\u001b[39mupdate(tensordict_out)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/torchrl/envs/gym_like.py:189\u001b[0m, in \u001b[0;36mGymLikeEnv._step\u001b[0;34m(self, tensordict)\u001b[0m\n\u001b[1;32m    186\u001b[0m reward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreward_spec\u001b[39m.\u001b[39mzero()\n\u001b[1;32m    187\u001b[0m \u001b[39mfor\u001b[39;00m _ \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwrapper_frame_skip):\n\u001b[1;32m    188\u001b[0m     obs, _reward, done, \u001b[39m*\u001b[39minfo \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output_transform(\n\u001b[0;32m--> 189\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_env\u001b[39m.\u001b[39;49mstep(action_np)\n\u001b[1;32m    190\u001b[0m     )\n\u001b[1;32m    191\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(obs, \u001b[39mlist\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(obs) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m    192\u001b[0m         \u001b[39m# Until gym 0.25.2 we had rendered frames returned in lists of length 1\u001b[39;00m\n\u001b[1;32m    193\u001b[0m         obs \u001b[39m=\u001b[39m obs[\u001b[39m0\u001b[39m]\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/gymnasium/wrappers/time_limit.py:57\u001b[0m, in \u001b[0;36mTimeLimit.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstep\u001b[39m(\u001b[39mself\u001b[39m, action):\n\u001b[1;32m     47\u001b[0m     \u001b[39m\"\"\"Steps through the environment and if the number of steps elapsed exceeds ``max_episode_steps`` then truncate.\u001b[39;00m\n\u001b[1;32m     48\u001b[0m \n\u001b[1;32m     49\u001b[0m \u001b[39m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     55\u001b[0m \n\u001b[1;32m     56\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m     observation, reward, terminated, truncated, info \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menv\u001b[39m.\u001b[39;49mstep(action)\n\u001b[1;32m     58\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_elapsed_steps \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m     60\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_elapsed_steps \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_max_episode_steps:\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/gymnasium/wrappers/order_enforcing.py:56\u001b[0m, in \u001b[0;36mOrderEnforcing.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_has_reset:\n\u001b[1;32m     55\u001b[0m     \u001b[39mraise\u001b[39;00m ResetNeeded(\u001b[39m\"\u001b[39m\u001b[39mCannot call env.step() before calling env.reset()\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 56\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menv\u001b[39m.\u001b[39;49mstep(action)\n",
      "File \u001b[0;32m/usr/lib/python3.10/site-packages/gymnasium/envs/box2d/car_racing.py:537\u001b[0m, in \u001b[0;36mCarRacing.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m    535\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcontinuous:\n\u001b[1;32m    536\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcar\u001b[39m.\u001b[39msteer(\u001b[39m-\u001b[39maction[\u001b[39m0\u001b[39m])\n\u001b[0;32m--> 537\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcar\u001b[39m.\u001b[39mgas(action[\u001b[39m1\u001b[39;49m])\n\u001b[1;32m    538\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcar\u001b[39m.\u001b[39mbrake(action[\u001b[39m2\u001b[39m])\n\u001b[1;32m    539\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    }
   ],
   "source": [
    "logs = defaultdict(list)\n",
    "pbar = tqdm(total=total_frames * frame_skip)\n",
    "eval_str = \"\"\n",
    "\n",
    "for i, tensordict_data in enumerate(collector):\n",
    "    for _ in range(num_epochs):\n",
    "        with torch.no_grad():\n",
    "            advantage_module(tensordict_data)\n",
    "        \n",
    "        data_view = tensordict_data.reshape(-1)\n",
    "        replay_buffer.extend(data_view.cpu())\n",
    "        for _ in range(frames_per_batch // sub_batch_size):\n",
    "            subdata = replay_buffer.sample(sub_batch_size)\n",
    "            loss_vals = loss_module(subdata.to(device))\n",
    "            loss_value = (\n",
    "                loss_vals[\"loss_objective\"]\n",
    "                + loss_vals[\"loss_entropy\"]\n",
    "                + loss_vals[\"loss_critic\"]\n",
    "            )\n",
    "\n",
    "            loss_value.backward()\n",
    "\n",
    "            torch.nn.utils.clip_grad_norm_(loss_module.parameters(), max_grad_norm)\n",
    "            optim.step()\n",
    "            optim.zero_grad()\n",
    "    \n",
    "    logs[\"reward\"].append(tensordict_data[\"next\", \"reward\"].mean().item())\n",
    "    pbar.update(tensordict_data.numel() * frame_skip)\n",
    "\n",
    "    cum_reward_str = (\n",
    "        f\"average reward={logs['reward'][-1]: 4.4f} (init={logs['reward'][0]: 4.4f})\"\n",
    "    )\n",
    "\n",
    "    logs[\"step_count\"].append(tensordict_data[\"step_count\"].max().item())\n",
    "    step_count_str = f\"step count(max): {logs['step_count'][-1]}\"\n",
    "\n",
    "    logs[\"lr\"].append(optim.param_groups[0][\"lr\"])\n",
    "    lr_str = f\"lr policy: {logs['lr'][-1]: 4.4f}\"\n",
    "\n",
    "    if i % 10 == 0:\n",
    "        with set_exploration_type(ExplorationType.MEAN), torch.no_grad():\n",
    "            eval_rollout = base_env.rollout(1000, policy_module)\n",
    "            logs[\"eval_reward\"].append(eval_rollout[\"next\", \"reward\"].mean().item())\n",
    "            logs[\"eval reward(sum)\"].append(\n",
    "                eval_rollout[\"next\", \"reward\"].sum().item()\n",
    "            )\n",
    "            logs[\"eval_step_count\"].append(eval_rollout[\"step_count\"].max().item())\n",
    "            eval_str = (\n",
    "                f\"eval cumulative reward={logs['eval reward(sum)'][-1]: 4.4f} \"\n",
    "                f\"init={logs['eval reward(sum)'][0]: 4.4f} \"\n",
    "                f\"eval step count(max): {logs['eval_step_count'][-1]}\"\n",
    "            )\n",
    "\n",
    "            del eval_rollout\n",
    "    pbar.set_description(\", \".join([eval_str, cum_reward_str, step_count_str, lr_str]))\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(logs[\"reward\"])\n",
    "plt.title(\"training rewards (average)\")\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(logs[\"step_count\"])\n",
    "plt.title(\"Max step count (training)\")\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(logs[\"eval reward (sum)\"])\n",
    "plt.title(\"Return (test)\")\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(logs[\"eval step_count\"])\n",
    "plt.title(\"Max step count (test)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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