5 - Proximal Policy Optimization (PPO) [CartPole].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://medium.com/@jonathan_hui/rl-trust-region-policy-optimization-trpo-explained-a6ee04eeeee9\n",
    "https://stackoverflow.com/a/50663200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torch.distributions as distributions\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_env = gym.make('CartPole-v1')\n",
    "test_env = gym.make('CartPole-v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "\n",
    "train_env.seed(SEED);\n",
    "test_env.seed(SEED+1);\n",
    "np.random.seed(SEED);\n",
    "torch.manual_seed(SEED);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, dropout = 0.5):\n",
    "        super().__init__()\n",
    "\n",
    "        self.fc_1 = nn.Linear(input_dim, hidden_dim)\n",
    "        self.fc_2 = nn.Linear(hidden_dim, output_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.fc_1(x)\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc_2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ActorCritic(nn.Module):\n",
    "    def __init__(self, actor, critic):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.actor = actor\n",
    "        self.critic = critic\n",
    "        \n",
    "    def forward(self, state):\n",
    "        \n",
    "        action_pred = self.actor(state)\n",
    "        value_pred = self.critic(state)\n",
    "        \n",
    "        return action_pred, value_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = train_env.observation_space.shape[0]\n",
    "HIDDEN_DIM = 128\n",
    "OUTPUT_DIM = train_env.action_space.n\n",
    "\n",
    "actor = MLP(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM)\n",
    "critic = MLP(INPUT_DIM, HIDDEN_DIM, 1)\n",
    "\n",
    "policy = ActorCritic(actor, critic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        torch.nn.init.xavier_normal_(m.weight)\n",
    "        m.bias.data.fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ActorCritic(\n",
       "  (actor): MLP(\n",
       "    (fc_1): Linear(in_features=4, out_features=128, bias=True)\n",
       "    (fc_2): Linear(in_features=128, out_features=2, bias=True)\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "  )\n",
       "  (critic): MLP(\n",
       "    (fc_1): Linear(in_features=4, out_features=128, bias=True)\n",
       "    (fc_2): Linear(in_features=128, out_features=1, bias=True)\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "policy.apply(init_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.01\n",
    "\n",
    "optimizer = optim.Adam(policy.parameters(), lr = LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(env, policy, optimizer, discount_factor, ppo_steps, ppo_clip):\n",
    "        \n",
    "    policy.train()\n",
    "        \n",
    "    states = []\n",
    "    actions = []\n",
    "    log_prob_actions = []\n",
    "    values = []\n",
    "    rewards = []\n",
    "    done = False\n",
    "    episode_reward = 0\n",
    "\n",
    "    state = env.reset()\n",
    "\n",
    "    while not done:\n",
    "\n",
    "        state = torch.FloatTensor(state).unsqueeze(0)\n",
    "\n",
    "        #append state here, not after we get the next state from env.step()\n",
    "        states.append(state)\n",
    "        \n",
    "        action_pred, value_pred = policy(state)\n",
    "                \n",
    "        action_prob = F.softmax(action_pred, dim = -1)\n",
    "                \n",
    "        dist = distributions.Categorical(action_prob)\n",
    "        \n",
    "        action = dist.sample()\n",
    "        \n",
    "        log_prob_action = dist.log_prob(action)\n",
    "        \n",
    "        state, reward, done, _ = env.step(action.item())\n",
    "\n",
    "        actions.append(action)\n",
    "        log_prob_actions.append(log_prob_action)\n",
    "        values.append(value_pred)\n",
    "        rewards.append(reward)\n",
    "        \n",
    "        episode_reward += reward\n",
    "    \n",
    "    states = torch.cat(states)\n",
    "    actions = torch.cat(actions)    \n",
    "    log_prob_actions = torch.cat(log_prob_actions)\n",
    "    values = torch.cat(values).squeeze(-1)\n",
    "    \n",
    "    returns = calculate_returns(rewards, discount_factor)\n",
    "    advantages = calculate_advantages(returns, values)\n",
    "    \n",
    "    policy_loss, value_loss = update_policy(policy, states, actions, log_prob_actions, advantages, returns, optimizer, ppo_steps, ppo_clip)\n",
    "\n",
    "    return policy_loss, value_loss, episode_reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_returns(rewards, discount_factor, normalize = True):\n",
    "    \n",
    "    returns = []\n",
    "    R = 0\n",
    "    \n",
    "    for r in reversed(rewards):\n",
    "        R = r + R * discount_factor\n",
    "        returns.insert(0, R)\n",
    "        \n",
    "    returns = torch.tensor(returns)\n",
    "    \n",
    "    if normalize:\n",
    "        returns = (returns - returns.mean()) / returns.std()\n",
    "        \n",
    "    return returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_advantages(returns, values, normalize = True):\n",
    "    \n",
    "    advantages = returns - values\n",
    "    \n",
    "    if normalize:\n",
    "        \n",
    "        advantages = (advantages - advantages.mean()) / advantages.std()\n",
    "        \n",
    "    return advantages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_policy(policy, states, actions, log_prob_actions, advantages, returns, optimizer, ppo_steps, ppo_clip):\n",
    "    \n",
    "    total_policy_loss = 0 \n",
    "    total_value_loss = 0\n",
    "    \n",
    "    advantages = advantages.detach()\n",
    "    log_prob_actions = log_prob_actions.detach()\n",
    "    actions = actions.detach()\n",
    "    \n",
    "    for _ in range(ppo_steps):\n",
    "                \n",
    "        #get new log prob of actions for all input states\n",
    "        action_pred, value_pred = policy(states)\n",
    "        value_pred = value_pred.squeeze(-1)\n",
    "        action_prob = F.softmax(action_pred, dim = -1)\n",
    "        dist = distributions.Categorical(action_prob)\n",
    "        \n",
    "        #new log prob using old actions\n",
    "        new_log_prob_actions = dist.log_prob(actions)\n",
    "        \n",
    "        policy_ratio = (new_log_prob_actions - log_prob_actions).exp()\n",
    "                \n",
    "        policy_loss_1 = policy_ratio * advantages\n",
    "        policy_loss_2 = torch.clamp(policy_ratio, min = 1.0 - ppo_clip, max = 1.0 + ppo_clip) * advantages\n",
    "        \n",
    "        policy_loss = - torch.min(policy_loss_1, policy_loss_2).sum()\n",
    "        \n",
    "        value_loss = F.smooth_l1_loss(returns, value_pred).sum()\n",
    "    \n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        policy_loss.backward()\n",
    "        value_loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "    \n",
    "        total_policy_loss += policy_loss.item()\n",
    "        total_value_loss += value_loss.item()\n",
    "    \n",
    "    return total_policy_loss / ppo_steps, total_value_loss / ppo_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(env, policy):\n",
    "    \n",
    "    policy.eval()\n",
    "    \n",
    "    rewards = []\n",
    "    done = False\n",
    "    episode_reward = 0\n",
    "\n",
    "    state = env.reset()\n",
    "\n",
    "    while not done:\n",
    "\n",
    "        state = torch.FloatTensor(state).unsqueeze(0)\n",
    "\n",
    "        with torch.no_grad():\n",
    "        \n",
    "            action_pred, _ = policy(state)\n",
    "\n",
    "            action_prob = F.softmax(action_pred, dim = -1)\n",
    "                \n",
    "        action = torch.argmax(action_prob, dim = -1)\n",
    "                \n",
    "        state, reward, done, _ = env.step(action.item())\n",
    "\n",
    "        episode_reward += reward\n",
    "        \n",
    "    return episode_reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Episode:  10 | Mean Train Rewards:  40.2 | Mean Test Rewards:  71.2 |\n",
      "| Episode:  20 | Mean Train Rewards:  57.1 | Mean Test Rewards: 156.4 |\n",
      "| Episode:  30 | Mean Train Rewards:  79.5 | Mean Test Rewards: 177.8 |\n",
      "| Episode:  40 | Mean Train Rewards: 180.6 | Mean Test Rewards: 291.9 |\n",
      "| Episode:  50 | Mean Train Rewards: 275.4 | Mean Test Rewards: 380.0 |\n",
      "Reached reward threshold in 58 episodes\n"
     ]
    }
   ],
   "source": [
    "MAX_EPISODES = 500\n",
    "DISCOUNT_FACTOR = 0.99\n",
    "N_TRIALS = 25\n",
    "REWARD_THRESHOLD = 475\n",
    "PRINT_EVERY = 10\n",
    "PPO_STEPS = 5\n",
    "PPO_CLIP = 0.2\n",
    "\n",
    "train_rewards = []\n",
    "test_rewards = []\n",
    "\n",
    "for episode in range(1, MAX_EPISODES+1):\n",
    "    \n",
    "    policy_loss, value_loss, train_reward = train(train_env, policy, optimizer, DISCOUNT_FACTOR, PPO_STEPS, PPO_CLIP)\n",
    "    \n",
    "    test_reward = evaluate(test_env, policy)\n",
    "    \n",
    "    train_rewards.append(train_reward)\n",
    "    test_rewards.append(test_reward)\n",
    "    \n",
    "    mean_train_rewards = np.mean(train_rewards[-N_TRIALS:])\n",
    "    mean_test_rewards = np.mean(test_rewards[-N_TRIALS:])\n",
    "    \n",
    "    if episode % PRINT_EVERY == 0:\n",
    "    \n",
    "        print(f'| Episode: {episode:3} | Mean Train Rewards: {mean_train_rewards:5.1f} | Mean Test Rewards: {mean_test_rewards:5.1f} |')\n",
    "    \n",
    "    if mean_test_rewards >= REWARD_THRESHOLD:\n",
    "        \n",
    "        print(f'Reached reward threshold in {episode} episodes')\n",
    "        \n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(test_rewards, label='Test Reward')\n",
    "plt.plot(train_rewards, label='Train Reward')\n",
    "plt.xlabel('Episode', fontsize=20)\n",
    "plt.ylabel('Reward', fontsize=20)\n",
    "plt.hlines(REWARD_THRESHOLD, 0, len(test_rewards), color='r')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}