216_cartpole_dqn_single_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DQN - single network\n",
    "\n",
    "- ReplayBuffer\n",
    "- Qnet\n",
    "- train()\n",
    "- main()\n",
    "\n",
    "## Observation space\n",
    "\n",
    "- force = -1 or 1\n",
    "- x, x_dot, theta, theta_dot = observation\n",
    "- -2.4 <= x <= 2.4\n",
    "- -inf < x_dot < inf\n",
    "- -12º <= theta <= 12º\n",
    "- -inf < theta_dot < inf\n",
    "  \n",
    "## References\n",
    "\n",
    "- https://github.com/seungeunrho/minimalRL/blob/master/dqn.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from IPython.display import display, HTML\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "import gym\n",
    "from collections import deque\n",
    "import random\n",
    "import math\n",
    "from copy import deepcopy\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayBuffer():\n",
    "    def __init__(self, buffer_limit = 50000):\n",
    "        self.buffer = deque(maxlen=buffer_limit)\n",
    "    \n",
    "    def put(self, transition):\n",
    "        self.buffer.append(transition)\n",
    "    \n",
    "    def sample(self, n):\n",
    "        mini_batch = random.sample(self.buffer, n)\n",
    "        state_list, action_list, reward_list, next_state_list, running_mask_list = [], [], [], [], []\n",
    "        \n",
    "        for transition in mini_batch:\n",
    "            state, action, reward, next_state, running_mask = transition\n",
    "            state_list.append(state)\n",
    "            action_list.append([action])\n",
    "            reward_list.append([reward])\n",
    "            next_state_list.append(next_state)\n",
    "            running_mask_list.append([running_mask])\n",
    "\n",
    "        return torch.tensor(state_list, dtype=torch.float), torch.tensor(action_list), \\\n",
    "               torch.tensor(reward_list), torch.tensor(next_state_list, dtype=torch.float), \\\n",
    "               torch.tensor(running_mask_list)\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.buffer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Qnet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Qnet, self).__init__()\n",
    "        self.fc1 = nn.Linear(4, 32)\n",
    "        self.fc2 = nn.Linear(32, 32)\n",
    "        self.fc3 = nn.Linear(32, 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "      \n",
    "    def sample_action(self, obs, epsilon):\n",
    "        coin = random.random()\n",
    "        if coin < epsilon:\n",
    "            return random.randint(0,1)\n",
    "        else:\n",
    "            out = self.forward(obs)\n",
    "            return out.argmax().item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rewards(scores, epsilons):\n",
    "    plt.plot(\n",
    "        tuple(range(len(scores))),\n",
    "        tuple(h for h in scores)\n",
    "    )\n",
    "    plt.draw()\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(\n",
    "        tuple(range(len(epsilons))),\n",
    "        tuple(h for h in epsilons)\n",
    "    )\n",
    "    plt.draw()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(q, epsilon, env, replay_buffer):\n",
    "    state = env.reset()\n",
    "\n",
    "    for i in range(1000):\n",
    "        action = q.sample_action(torch.from_numpy(state).float(), epsilon)\n",
    "\n",
    "        next_state, reward, done, info = env.step(action)\n",
    "        running_mask = 0.0 if done else 1.0\n",
    "\n",
    "        replay_buffer.put((state, action, reward, next_state, running_mask))\n",
    "            \n",
    "        if done:\n",
    "            return i\n",
    "        else:\n",
    "            state = next_state\n",
    "    \n",
    "    raise Exception(\"Infinite loop encountered!\")\n",
    "    \n",
    "def train(q, memory, optimizer, gamma = 0.98, batch_size=32):\n",
    "    for _ in range(10):\n",
    "        state, action, reward, next_state, running_mask = memory.sample(batch_size)\n",
    "\n",
    "        q_out = q(state)\n",
    "        \n",
    "        q_action = q_out.gather(1,action)\n",
    "        \n",
    "        max_q_next = q(next_state).max(1)[0].unsqueeze(1)\n",
    "        target = reward + gamma * max_q_next * running_mask\n",
    "        \n",
    "        loss = F.smooth_l1_loss(q_action, target)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "def main(q_net, init_epsilon = 0.5, target_epsilon = 0.001, n_iterations = 10000, learning_rate = 0.0005):\n",
    "    assert target_epsilon <= init_epsilon\n",
    "\n",
    "    env = gym.make('CartPole-v1')\n",
    "    \n",
    "    replay_buffer = ReplayBuffer()    \n",
    "    q_net = deepcopy(q_net)\n",
    "    \n",
    "    all_rewards = []\n",
    "    all_epsilons = []\n",
    "    \n",
    "    avg_window = 50\n",
    "    avg_rewards = []\n",
    "    avg_epsilons = []\n",
    "    \n",
    "    epsilon = init_epsilon\n",
    "    decay = math.e ** (math.log(target_epsilon/epsilon) / (n_iterations - 1))\n",
    "    \n",
    "    optimizer = optim.Adam(q_net.parameters(), lr=learning_rate)\n",
    "\n",
    "    for i in range(n_iterations):\n",
    "        reward = run(q_net, epsilon, env, replay_buffer)\n",
    "        all_rewards.append(reward)\n",
    "        all_epsilons.append(epsilon)\n",
    "        \n",
    "        if len(replay_buffer) > 5000:\n",
    "            train(q_net, replay_buffer, optimizer)\n",
    "        \n",
    "        epsilon *= decay\n",
    "\n",
    "        if len(all_rewards) >= avg_window:\n",
    "            avg_reward = np.average(all_rewards[i-avg_window+1:i+1])\n",
    "            avg_epsilon = np.average(all_epsilons[i-avg_window+1:i+1])\n",
    "            \n",
    "            if avg_reward > 490 and epsilon < 0.05:\n",
    "                break\n",
    "            \n",
    "            avg_rewards.append(avg_reward)\n",
    "            avg_epsilons.append(avg_epsilon)\n",
    "            \n",
    "            if i % 1000 == 0:\n",
    "                print(i, avg_reward, avg_epsilon)\n",
    "    \n",
    "    print(i, avg_reward, avg_epsilon)\n",
    "    print(f\"epsilon={epsilon}\")\n",
    "    print(f\"q={q_net}\")\n",
    "    \n",
    "    env.close()\n",
    "\n",
    "    return q_net, all_rewards, avg_rewards, avg_epsilons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 62.18 0.2726946500083153\n",
      "2000 280.0 0.1464713298939324\n",
      "3000 326.18 0.07867352909286277\n",
      "4000 338.0 0.04225758163326341\n",
      "5000 280.78 0.02269763700804829\n",
      "6000 334.42 0.012191486257310867\n",
      "7000 285.04 0.006548361713137647\n",
      "8000 288.8 0.003517293972289274\n",
      "9000 280.24 0.0018892293110019374\n",
      "9999 215.34 0.0010153846789945958\n",
      "epsilon=0.0009993786701428952\n",
      "q=Qnet(\n",
      "  (fc1): Linear(in_features=4, out_features=32, bias=True)\n",
      "  (fc2): Linear(in_features=32, out_features=32, bias=True)\n",
      "  (fc3): Linear(in_features=32, out_features=2, bias=True)\n",
      ")\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q_net_0 = Qnet()\n",
    "q_net_1, all_rewards, avg_rewards, avg_epsilons = main(q_net_0)\n",
    "plot_rewards(avg_rewards, avg_epsilons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_frames_as_video(frames):\n",
    "    fig = plt.figure(figsize=(frames[0].shape[1]/72.0, frames[0].shape[0]/72.0), dpi=72)\n",
    "    plt.axis('off')\n",
    "    \n",
    "    ims = []\n",
    "    for i, frame in enumerate(frames):\n",
    "        im = plt.imshow(frame, animated=True)\n",
    "        ims.append([im])\n",
    "        \n",
    "    anim = animation.ArtistAnimation(fig, ims, interval=40, blit=True, repeat=False)\n",
    "    display(HTML(anim.to_html5_video()))\n",
    "    plt.close()\n",
    "\n",
    "def test(q):\n",
    "    env = gym.make('CartPole-v1')\n",
    "    env._max_episode_steps = 1000\n",
    "    state = env.reset()\n",
    "    \n",
    "    frames = []\n",
    "\n",
    "    for i in range(1000):\n",
    "        frame = env.render(mode=\"rgb_array\")\n",
    "        frames.append(frame)\n",
    "        action = q.sample_action(torch.from_numpy(state).float(), 0)\n",
    "        state, reward, done, info = env.step(action)\n",
    "        if done:\n",
    "            print(i)\n",
    "            break\n",
    "    env.close()\n",
    "    display_frames_as_video(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "782\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test(q_net_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# q_net_2, all_rewards, avg_rewards, avg_epsilons = main(q_net_1, init_epsilon=0.001, target_epsilon=0.0001, n_iterations=5000)\n",
    "# plot_rewards(avg_rewards, avg_epsilons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test(q_net_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"q_net_0\", [param.data for param in q_net_0.parameters()])\n",
    "# print(\"q_net_1\", [param.data for param in q_net_1.parameters()])\n",
    "# print(\"q_net_2\", [param.data for param in q_net_2.parameters()])"
   ]
  }
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