Cart-and-Pole-DQN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import gym\n",
    "import math\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import namedtuple\n",
    "from itertools import count\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torchvision.transforms as T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_ipython = 'inline' in matplotlib.get_backend()\n",
    "if is_ipython: from IPython import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQN(nn.Module):\n",
    "    \n",
    "    def __init__(self, img_height, img_width):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.fc1 = nn.Linear(in_features=img_height*img_width*3, out_features=24)\n",
    "        self.fc2 = nn.Linear(in_features=24, out_features=32)\n",
    "        self.out = nn.Linear(in_features=32, out_features=2)\n",
    "        \n",
    "    def forward(self, t):\n",
    "        t = t.flatten(start_dim=1)\n",
    "        t = F.relu(self.fc1(t))\n",
    "        t = F.relu(self.fc2(t))\n",
    "        t = self.out(t)\n",
    "        return t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Experience = namedtuple('Experience',('state', 'action', 'next_state', 'reward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = Experience(2,3,1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Experience(state=2, action=3, next_state=1, reward=4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayMemory():\n",
    "    def __init__(self, capacity):\n",
    "        self.capacity = capacity\n",
    "        self.memory = []\n",
    "        self.push_count = 0\n",
    "    \n",
    "    def push(self, experience):\n",
    "        if len(self.memory) < self.capacity:\n",
    "            self.memory.append(experience)\n",
    "        else:\n",
    "            self.memory[self.push_count % self.capacity] = experience\n",
    "        self.push_count += 1\n",
    "    \n",
    "    def sample(self, batch_size):\n",
    "        return random.sample(self.memory, batch_size)\n",
    "    \n",
    "    def can_provide_sample(self, batch_size):\n",
    "        return len(self.memory) >= batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EpsilonGreedyStrategy():\n",
    "    def __init__(self, start, end, decay):\n",
    "        self.start = start\n",
    "        self.end = end\n",
    "        self.decay = decay\n",
    "        \n",
    "    def get_exploration_rate(self, current_step):\n",
    "        return self.end + (self.start - self.end) * math.exp(-1.*current_step*self.decay )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent():\n",
    "    def __init__(self, strategy, num_actions, device):\n",
    "        self.current_step = 0\n",
    "        self.strategy = strategy\n",
    "        self.num_actions = num_actions\n",
    "        self.device = device\n",
    "    \n",
    "    def select_action(self, state, policy_net):\n",
    "        rate = strategy.get_exploration_rate(self.current_step)\n",
    "        self.current_step += 1\n",
    "        \n",
    "        if rate > random.random():\n",
    "            action =  random.randrange(self.num_actions)\n",
    "            return torch.tensor([action]).to(device)\n",
    "        else:\n",
    "            with torch.no_grad():\n",
    "                return policy_net(state).argmax(dim=1).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CartPoleEnvManager():\n",
    "    def __init__(self, device):\n",
    "        self.device = device\n",
    "        self.env = gym.make('CartPole-v0')\n",
    "        self.env.reset()\n",
    "        self.current_screen = None\n",
    "        self.done = False\n",
    "    \n",
    "    def reset(self):\n",
    "        self.env.reset()\n",
    "        self.current_screen = None\n",
    "    \n",
    "    def close(self):\n",
    "        self.env.close()\n",
    "        \n",
    "    def render(self, mode='human'):\n",
    "        return self.env.render(mode)\n",
    "    \n",
    "    def num_actions_available(self):\n",
    "        return self.env.action_space.n\n",
    "    \n",
    "    def take_action(self, action):\n",
    "        _, reward, self.done, _ = self.env.step(action.item())\n",
    "        return torch.tensor([reward], device=self.device)\n",
    "    \n",
    "    def just_starting(self):\n",
    "        return self.current_screen is None\n",
    "    \n",
    "    def get_state(self):\n",
    "        if self.just_starting() or self.done:\n",
    "            self.current_screen = self.get_processed_screen()\n",
    "            black_screen = torch.zeros_like(self.current_screen)\n",
    "            return black_screen\n",
    "        else:\n",
    "            s1 = self.current_screen\n",
    "            s2 = self.get_processed_screen()\n",
    "            self.current_screen = s2\n",
    "            return s2 - s1\n",
    "    \n",
    "    def get_screen_height(self):\n",
    "        screen = self.get_processed_screen()\n",
    "        return screen.shape[2]\n",
    "    \n",
    "    def get_screen_width(self):\n",
    "        screen = self.get_processed_screen()\n",
    "        return screen.shape[3]\n",
    "    \n",
    "    def get_processed_screen(self):\n",
    "        screen = self.render('rgb_array').transpose((2,0,1))\n",
    "        screen = self.crop_screen(screen)\n",
    "        return self.transform_screen_data(screen)\n",
    "\n",
    "    def crop_screen(self, screen):\n",
    "        screen_height = screen.shape[1]\n",
    "        top = int(screen_height * 0.4)\n",
    "        bottom = int(screen_height * 0.8)\n",
    "        screen = screen[:, top:bottom, :]\n",
    "        return screen\n",
    "\n",
    "    def transform_screen_data(self, screen):\n",
    "        screen = np.ascontiguousarray(screen, dtype=np.float32) / 255\n",
    "        screen = torch.from_numpy(screen)\n",
    "        \n",
    "        resize = T.Compose([\n",
    "            T.ToPILImage(),\n",
    "            T.Resize((40,90)),\n",
    "            T.ToTensor()\n",
    "        ])\n",
    "        \n",
    "        return resize(screen).unsqueeze(0).to(self.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device(\"cude\" if torch.cuda.is_available() else \"cpu\")\n",
    "em = CartPoleEnvManager(device)\n",
    "em.reset()\n",
    "screen = em.render('rgb_array')\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(screen)\n",
    "plt.title('Non processed screen exemple')\n",
    "plt.show()\n",
    "em.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "screen = em.get_processed_screen()\n",
    "plt.figure()\n",
    "plt.imshow(screen.squeeze(0).permute(1, 2, 0), interpolation='none')\n",
    "plt.title('Processed screen example')\n",
    "plt.show()\n",
    "em.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "screen = em.get_state()\n",
    "plt.figure()\n",
    "plt.imshow(screen.squeeze(0).permute(1, 2, 0), interpolation='none')\n",
    "plt.title('Processed screen example')\n",
    "plt.show()\n",
    "em.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    em.take_action(torch.tensor([1]))\n",
    "screen = em.get_state()\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(screen.squeeze(0).permute(1, 2, 0), interpolation='none')\n",
    "plt.title('Processed screen example')\n",
    "plt.show()\n",
    "em.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "em.done = True\n",
    "screen = em.get_state()\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(screen.squeeze(0).permute(1, 2, 0), interpolation='none')\n",
    "plt.title('Processed screen example')\n",
    "plt.show()\n",
    "em.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(values, moving_avg_period):\n",
    "    plt.figure(2)\n",
    "    plt.clf()\n",
    "    plt.title('Training ..')\n",
    "    plt.xlabel('Episode')\n",
    "    plt.ylabel('Duration')\n",
    "    plt.plot(values)\n",
    "    plt.plot(get_moving_averge(moving_avg_period, values))\n",
    "    plt.pause(0.001)\n",
    "    if is_ipython:\n",
    "        display.clear_output(wait=True)\n",
    "\n",
    "def get_moving_averge(period, values):\n",
    "    values = torch.tensor(values, dtype=torch.float)\n",
    "    if len(values) >= period:\n",
    "        moving_avg = values.unfold(0 ,period, 1).mean(dim=1).flatten(start_dim=0)\n",
    "        moving_avg = torch.cat((torch.zeros(period-1), moving_avg))\n",
    "        return moving_avg.numpy()\n",
    "    else:\n",
    "        moving_avg = torch.zeros(len(values))\n",
    "        return moving_avg.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(np.random.rand(300), 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "gamma = 0.999\n",
    "eps_start = 1\n",
    "eps_end = 0.01\n",
    "eps_decay = 0.001\n",
    "target_update = 10\n",
    "memory_size = 10000\n",
    "lr = 0.001\n",
    "num_episods = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "em = CartPoleEnvManager(device)\n",
    "strategy = EpsilonGreedyStrategy(eps_start, eps_end, eps_decay)\n",
    "agent = Agent(strategy, em.num_actions_available(), device)\n",
    "memory = ReplayMemory(memory_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy_net = DQN(em.get_screen_height(), em.get_screen_width()).to(device)\n",
    "target_net = DQN(em.get_screen_height(), em.get_screen_width()).to(device)\n",
    "target_net.load_state_dict(policy_net.state_dict())\n",
    "target_net.eval()\n",
    "optimizer = optim.Adam(params=policy_net.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_tensors(experiences):\n",
    "    batch = Experience(*zip(*experiences))\n",
    "    t1 = torch.cat(batch.state)\n",
    "    t2 = torch.cat(batch.action)\n",
    "    t3 = torch.cat(batch.reward)\n",
    "    t4 = torch.cat(batch.next_state)\n",
    "    \n",
    "    return (t1, t2, t3, t4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QValues():\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    \n",
    "    @staticmethod\n",
    "    def get_current(policy_net, state, actions):\n",
    "        return policy_net(states).gather(dim=1, index=actions.unsqueeze(-1))\n",
    "    \n",
    "    @staticmethod\n",
    "    def get_next(target_net, next_states):\n",
    "        final_state_locations = next_states.flatten(start_dim=1).max(dim=1)[0].eq(0).type(torch.bool)\n",
    "        non_final_states_locations = (final_state_locations == False)\n",
    "        non_final_states = next_states[non_final_states_locations]\n",
    "        batch_size = next_states.shape[0]\n",
    "        values = torch.zeros(batch_size).to(QValues.device)\n",
    "        values[non_final_states_locations] = target_net(non_final_states).max(dim=1)[0].detach()\n",
    "        return values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "episode_durations = []\n",
    "try:\n",
    "    for episode in range(num_episods):\n",
    "        em.reset()\n",
    "        state = em.get_state()\n",
    "\n",
    "        for timestep in count():\n",
    "            action = agent.select_action(state, policy_net)\n",
    "            reward = em.take_action(action)\n",
    "            next_state = em.get_state()\n",
    "            memory.push(Experience(state, action, next_state, reward))\n",
    "            state = next_state\n",
    "\n",
    "            if memory.can_provide_sample(batch_size):\n",
    "                experiences = memory.sample(batch_size)\n",
    "                states, actions, rewards, next_states = extract_tensors(experiences)\n",
    "\n",
    "                current_q_values = QValues.get_current(policy_net, states, actions)\n",
    "                next_q_values = QValues.get_next(target_net, next_states)\n",
    "                target_q_values = (next_q_values * gamma) + rewards\n",
    "\n",
    "                loss = F.mse_loss(current_q_values, target_q_values.unsqueeze(1))\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "            if em.done:\n",
    "                episode_durations.append(timestep)\n",
    "                plot(episode_durations, 100)\n",
    "                break\n",
    "\n",
    "        if episode % target_update == 0:\n",
    "            target_net.load_state_dict(policy_net.state_dict())\n",
    "finally:\n",
    "    em.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}