7.rainbow dqn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, random\n",
    "\n",
    "import gym\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.autograd as autograd \n",
    "import torch.nn.functional as F\n",
    "\n",
    "from common.layers import NoisyLinear\n",
    "from common.replay_buffer import ReplayBuffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Use Cuda</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_CUDA = torch.cuda.is_available()\n",
    "Variable = lambda *args, **kwargs: autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else autograd.Variable(*args, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Cart Pole Environment</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <type 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env_id = \"CartPole-v0\"\n",
    "env = gym.make(env_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Rainbow: Combining Improvements in Deep Reinforcement Learning</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RainbowDQN(nn.Module):\n",
    "    def __init__(self, num_inputs, num_actions, num_atoms, Vmin, Vmax):\n",
    "        super(RainbowDQN, self).__init__()\n",
    "        \n",
    "        self.num_inputs   = num_inputs\n",
    "        self.num_actions  = num_actions\n",
    "        self.num_atoms    = num_atoms\n",
    "        self.Vmin         = Vmin\n",
    "        self.Vmax         = Vmax\n",
    "        \n",
    "        self.linear1 = nn.Linear(num_inputs, 32)\n",
    "        self.linear2 = nn.Linear(32, 64)\n",
    "        \n",
    "        self.noisy_value1 = NoisyLinear(64, 64, use_cuda=USE_CUDA)\n",
    "        self.noisy_value2 = NoisyLinear(64, self.num_atoms, use_cuda=USE_CUDA)\n",
    "        \n",
    "        self.noisy_advantage1 = NoisyLinear(64, 64, use_cuda=USE_CUDA)\n",
    "        self.noisy_advantage2 = NoisyLinear(64, self.num_atoms * self.num_actions, use_cuda=USE_CUDA)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        \n",
    "        x = F.relu(self.linear1(x))\n",
    "        x = F.relu(self.linear2(x))\n",
    "        \n",
    "        value = F.relu(self.noisy_value1(x))\n",
    "        value = self.noisy_value2(value)\n",
    "        \n",
    "        advantage = F.relu(self.noisy_advantage1(x))\n",
    "        advantage = self.noisy_advantage2(advantage)\n",
    "        \n",
    "        value     = value.view(batch_size, 1, self.num_atoms)\n",
    "        advantage = advantage.view(batch_size, self.num_actions, self.num_atoms)\n",
    "        \n",
    "        x = value + advantage - advantage.mean(1, keepdim=True)\n",
    "        x = F.softmax(x.view(-1, self.num_atoms)).view(-1, self.num_actions, self.num_atoms)\n",
    "        \n",
    "        return x\n",
    "        \n",
    "    def reset_noise(self):\n",
    "        self.noisy_value1.reset_noise()\n",
    "        self.noisy_value2.reset_noise()\n",
    "        self.noisy_advantage1.reset_noise()\n",
    "        self.noisy_advantage2.reset_noise()\n",
    "    \n",
    "    def act(self, state):\n",
    "        state = Variable(torch.FloatTensor(state).unsqueeze(0), volatile=True)\n",
    "        dist = self.forward(state).data.cpu()\n",
    "        dist = dist * torch.linspace(self.Vmin, self.Vmax, self.num_atoms)\n",
    "        action = dist.sum(2).max(1)[1].numpy()[0]\n",
    "        return action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_atoms = 51\n",
    "Vmin = -10\n",
    "Vmax = 10\n",
    "\n",
    "current_model = RainbowDQN(env.observation_space.shape[0], env.action_space.n, num_atoms, Vmin, Vmax)\n",
    "target_model  = RainbowDQN(env.observation_space.shape[0], env.action_space.n, num_atoms, Vmin, Vmax)\n",
    "\n",
    "if USE_CUDA:\n",
    "    current_model = current_model.cuda()\n",
    "    target_model  = target_model.cuda()\n",
    "    \n",
    "optimizer = optim.Adam(current_model.parameters(), 0.001)\n",
    "\n",
    "replay_buffer = ReplayBuffer(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_target(current_model, target_model):\n",
    "    target_model.load_state_dict(current_model.state_dict())\n",
    "    \n",
    "update_target(current_model, target_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def projection_distribution(next_state, rewards, dones):\n",
    "    batch_size  = next_state.size(0)\n",
    "    \n",
    "    delta_z = float(Vmax - Vmin) / (num_atoms - 1)\n",
    "    support = torch.linspace(Vmin, Vmax, num_atoms)\n",
    "    \n",
    "    next_dist   = target_model(next_state).data.cpu() * support\n",
    "    next_action = next_dist.sum(2).max(1)[1]\n",
    "    next_action = next_action.unsqueeze(1).unsqueeze(1).expand(next_dist.size(0), 1, next_dist.size(2))\n",
    "    next_dist   = next_dist.gather(1, next_action).squeeze(1)\n",
    "        \n",
    "    rewards = rewards.unsqueeze(1).expand_as(next_dist)\n",
    "    dones   = dones.unsqueeze(1).expand_as(next_dist)\n",
    "    support = support.unsqueeze(0).expand_as(next_dist)\n",
    "    \n",
    "    Tz = rewards + (1 - dones) * 0.99 * support\n",
    "    Tz = Tz.clamp(min=Vmin, max=Vmax)\n",
    "    b  = (Tz - Vmin) / delta_z\n",
    "    l  = b.floor().long()\n",
    "    u  = b.ceil().long()\n",
    "        \n",
    "    offset = torch.linspace(0, (batch_size - 1) * num_atoms, batch_size).long()\\\n",
    "                    .unsqueeze(1).expand(batch_size, num_atoms)\n",
    "\n",
    "    proj_dist = torch.zeros(next_dist.size())    \n",
    "    proj_dist.view(-1).index_add_(0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1))\n",
    "    proj_dist.view(-1).index_add_(0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1))\n",
    "        \n",
    "    return proj_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Computing Temporal Difference Loss</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_td_loss(batch_size):\n",
    "    state, action, reward, next_state, done = replay_buffer.sample(batch_size) \n",
    "\n",
    "    state      = Variable(torch.FloatTensor(np.float32(state)))\n",
    "    next_state = Variable(torch.FloatTensor(np.float32(next_state)), volatile=True)\n",
    "    action     = Variable(torch.LongTensor(action))\n",
    "    reward     = torch.FloatTensor(reward)\n",
    "    done       = torch.FloatTensor(np.float32(done))\n",
    "\n",
    "    proj_dist = projection_distribution(next_state, reward, done)\n",
    "    \n",
    "    dist = current_model(state)\n",
    "    action = action.unsqueeze(1).unsqueeze(1).expand(batch_size, 1, num_atoms)\n",
    "    dist = dist.gather(1, action).squeeze(1)\n",
    "    dist.data.clamp_(0.01, 0.99)\n",
    "    loss = -(Variable(proj_dist) * dist.log()).sum(1)\n",
    "    loss  = loss.mean()\n",
    "        \n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    current_model.reset_noise()\n",
    "    target_model.reset_noise()\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(frame_idx, rewards, losses):\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(20,5))\n",
    "    plt.subplot(131)\n",
    "    plt.title('frame %s. reward: %s' % (frame_idx, np.mean(rewards[-10:])))\n",
    "    plt.plot(rewards)\n",
    "    plt.subplot(132)\n",
    "    plt.title('loss')\n",
    "    plt.plot(losses)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Training</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv4AAAE/CAYAAAA+Occ1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nShWA/weLKyFwyGPfjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f81884de5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_frames = 15000\n",
    "batch_size = 32\n",
    "gamma      = 0.99\n",
    "\n",
    "losses = []\n",
    "all_rewards = []\n",
    "episode_reward = 0\n",
    "\n",
    "state = env.reset()\n",
    "for frame_idx in range(1, num_frames + 1):\n",
    "    action = current_model.act(state)\n",
    "    \n",
    "    next_state, reward, done, _ = env.step(action)\n",
    "    replay_buffer.push(state, action, reward, next_state, done)\n",
    "    \n",
    "    state = next_state\n",
    "    episode_reward += reward\n",
    "    \n",
    "    if done:\n",
    "        state = env.reset()\n",
    "        all_rewards.append(episode_reward)\n",
    "        episode_reward = 0\n",
    "        \n",
    "    if len(replay_buffer) > batch_size:\n",
    "        loss = compute_td_loss(batch_size)\n",
    "        losses.append(loss.data[0])\n",
    "        \n",
    "    if frame_idx % 200 == 0:\n",
    "        plot(frame_idx, all_rewards, losses)\n",
    "        \n",
    "    if frame_idx % 1000 == 0:\n",
    "        update_target(current_model, target_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><hr></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Atari Environment</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from common.wrappers import make_atari, wrap_deepmind, wrap_pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_id = \"PongNoFrameskip-v4\"\n",
    "env    = make_atari(env_id)\n",
    "env    = wrap_deepmind(env)\n",
    "env    = wrap_pytorch(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RainbowCnnDQN(nn.Module):\n",
    "    def __init__(self, input_shape, num_actions, num_atoms, Vmin, Vmax):\n",
    "        super(RainbowCnnDQN, self).__init__()\n",
    "        \n",
    "        self.input_shape   = input_shape\n",
    "        self.num_actions  = num_actions\n",
    "        self.num_atoms    = num_atoms\n",
    "        self.Vmin         = Vmin\n",
    "        self.Vmax         = Vmax\n",
    "        \n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(32, 64, kernel_size=4, stride=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, stride=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        \n",
    "        self.noisy_value1 = NoisyLinear(self.feature_size(), 512, use_cuda=USE_CUDA)\n",
    "        self.noisy_value2 = NoisyLinear(512, self.num_atoms, use_cuda=USE_CUDA)\n",
    "        \n",
    "        self.noisy_advantage1 = NoisyLinear(self.feature_size(), 512, use_cuda=USE_CUDA)\n",
    "        self.noisy_advantage2 = NoisyLinear(512, self.num_atoms * self.num_actions, use_cuda=USE_CUDA)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        \n",
    "        x = x / 255.\n",
    "        x = self.features(x)\n",
    "        x = x.view(batch_size, -1)\n",
    "        \n",
    "        value = F.relu(self.noisy_value1(x))\n",
    "        value = self.noisy_value2(value)\n",
    "        \n",
    "        advantage = F.relu(self.noisy_advantage1(x))\n",
    "        advantage = self.noisy_advantage2(advantage)\n",
    "        \n",
    "        value     = value.view(batch_size, 1, self.num_atoms)\n",
    "        advantage = advantage.view(batch_size, self.num_actions, self.num_atoms)\n",
    "        \n",
    "        x = value + advantage - advantage.mean(1, keepdim=True)\n",
    "        x = F.softmax(x.view(-1, self.num_atoms)).view(-1, self.num_actions, self.num_atoms)\n",
    "        \n",
    "        return x\n",
    "        \n",
    "    def reset_noise(self):\n",
    "        self.noisy_value1.reset_noise()\n",
    "        self.noisy_value2.reset_noise()\n",
    "        self.noisy_advantage1.reset_noise()\n",
    "        self.noisy_advantage2.reset_noise()\n",
    "        \n",
    "    def feature_size(self):\n",
    "        return self.features(autograd.Variable(torch.zeros(1, *self.input_shape))).view(1, -1).size(1)\n",
    "    \n",
    "    def act(self, state):\n",
    "        state = Variable(torch.FloatTensor(np.float32(state)).unsqueeze(0), volatile=True)\n",
    "        dist = self.forward(state).data.cpu()\n",
    "        dist = dist * torch.linspace(self.Vmin, self.Vmax, self.num_atoms)\n",
    "        action = dist.sum(2).max(1)[1].numpy()[0]\n",
    "        return action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_atoms = 51\n",
    "Vmin = -10\n",
    "Vmax = 10\n",
    "\n",
    "current_model = RainbowCnnDQN(env.observation_space.shape, env.action_space.n, num_atoms, Vmin, Vmax)\n",
    "target_model  = RainbowCnnDQN(env.observation_space.shape, env.action_space.n, num_atoms, Vmin, Vmax)\n",
    "\n",
    "if USE_CUDA:\n",
    "    current_model = current_model.cuda()\n",
    "    target_model  = target_model.cuda()\n",
    "    \n",
    "optimizer = optim.Adam(current_model.parameters(), lr=0.0001)\n",
    "update_target(current_model, target_model)\n",
    "\n",
    "replay_initial = 10000\n",
    "replay_buffer  = ReplayBuffer(100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAE/CAYAAADxMqTfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f818961ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_frames = 1000000\n",
    "batch_size = 32\n",
    "gamma      = 0.99\n",
    "\n",
    "losses = []\n",
    "all_rewards = []\n",
    "episode_reward = 0\n",
    "\n",
    "state = env.reset()\n",
    "for frame_idx in range(1, num_frames + 1):\n",
    "    action = current_model.act(state)\n",
    "    \n",
    "    next_state, reward, done, _ = env.step(action)\n",
    "    replay_buffer.push(state, action, reward, next_state, done)\n",
    "    \n",
    "    state = next_state\n",
    "    episode_reward += reward\n",
    "    \n",
    "    if done:\n",
    "        state = env.reset()\n",
    "        all_rewards.append(episode_reward)\n",
    "        episode_reward = 0\n",
    "        \n",
    "    if len(replay_buffer) > replay_initial:\n",
    "        loss = compute_td_loss(batch_size)\n",
    "        losses.append(loss.data[0])\n",
    "        \n",
    "    if frame_idx % 10000 == 0:\n",
    "        plot(frame_idx, all_rewards, losses)\n",
    "        \n",
    "    if frame_idx % 1000 == 0:\n",
    "        update_target(current_model, target_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}