CartPole-A2C-nb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CartPole Env\n",
    "import gym\n",
    "import matplotlib as plt\n",
    "\n",
    "class CartPoleEnv:\n",
    "    def __init__(self):\n",
    "        self.fps = 30\n",
    "        self.env = gym.make('CartPole-v0')  # environment interface to game\n",
    "        self.env.seed(42)\n",
    "        self.env.reset()\n",
    "        self.is_show = False\n",
    "        self.current_sate = []\n",
    "        self.is_done = False\n",
    "\n",
    "    def render(self):\n",
    "        self.env.render()\n",
    "        \n",
    "    def reset(self, is_show = False):\n",
    "        state = self.env.reset()\n",
    "        state = state.tolist()\n",
    "        self.current_sate = state\n",
    "        self.is_done = False\n",
    "        self.is_show = is_show\n",
    "        if self.is_show:\n",
    "            self.env.render()\n",
    "\n",
    "        return state\n",
    "    \n",
    "    def act(self, action):\n",
    "        if self.is_show:\n",
    "            self.env.render()\n",
    "        # return observation, reward, done, info\n",
    "        state, reward, is_done, info = self.env.step(self.get_action_set()[action])\n",
    "        state = state.tolist()\n",
    "        self.current_sate = state\n",
    "        self.is_done = is_done\n",
    "        return state, reward, is_done, info\n",
    "\n",
    "    def get_num_actions(self):\n",
    "        return 2\n",
    "\n",
    "    def get_action_set(self):\n",
    "        return [0, 1]\n",
    "\n",
    "    def get_screen_rgb(self):\n",
    "        pass\n",
    "\n",
    "    def get_screen_gray(self):\n",
    "        pass\n",
    "\n",
    "    def get_num_state_features(self):\n",
    "        return 4\n",
    "\n",
    "    def get_state(self):\n",
    "        return self.current_sate\n",
    "\n",
    "    def is_over(self):\n",
    "        return self.is_done\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "class EpsilonGreedy:\n",
    "    def __init__(self, epsilon, num_action, min_epsilon = 0.01, decay = 0.99):\n",
    "        self.epsilon = epsilon\n",
    "        self.num_action = num_action\n",
    "        self.min_epsilon = min_epsilon\n",
    "        self.decay = decay\n",
    "        self.action_time = 0\n",
    "\n",
    "    def select_action(self):\n",
    "        self.action_time += 1\n",
    "        if np.random.rand() < self.epsilon:\n",
    "            self.update_epsilon()\n",
    "            return np.random.choice(self.num_action)\n",
    "        else: \n",
    "            self.update_epsilon()\n",
    "            return -1\n",
    "    \n",
    "    def update_epsilon(self):\n",
    "        self.epsilon = max(self.min_epsilon, min(0.5, 0.99**(self.action_time / 30)))\n",
    "    \n",
    "    def shutdown_explore(self):\n",
    "        self.epsilon = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "class Agent:\n",
    "    def __init__(self, state_size, num_action, reward_discount, learning_rate, exploration_strategy):\n",
    "        self.state_size = state_size\n",
    "        self.num_action = num_action\n",
    "        self.reward_discount = reward_discount\n",
    "        self.exploration_strategy = exploration_strategy\n",
    "        self.iter = 0\n",
    "        self.eps = 0\n",
    "        self.data_type = tf.float32\n",
    "        self.optimizer = tf.keras.optimizers.Adam(learning_rate = learning_rate)\n",
    "        self.avg_loss = tf.keras.metrics.Mean(name = 'loss')\n",
    "        self.model = self.build_model('model')\n",
    "        self.is_shutdown_explore = False\n",
    "\n",
    "        # For A2C loss function coefficients\n",
    "        self.coef_entropy = 0\n",
    "        self.coef_value = 1\n",
    "\n",
    "    def build_model(self, name):\n",
    "        # # Shared layers\n",
    "        # nn_input = tf.keras.Input(shape = self.state_size, dtype = self.data_type)\n",
    "        # x = tf.keras.layers.Dense(units = 64)(nn_input)\n",
    "        # x = tf.keras.layers.ReLU()(x)\n",
    "        # x = tf.keras.layers.Dense(units = 128)(x)\n",
    "        # common = tf.keras.layers.ReLU()(x)\n",
    "\n",
    "        # # Actor Model\n",
    "        # actor_layer = tf.keras.layers.Dense(units = 64)(common)\n",
    "        # actor_layer = tf.keras.layers.ReLU()(actor_layer)\n",
    "        # actor_layer = tf.keras.layers.Dense(units = self.num_action)(actor_layer)\n",
    "        # actor_nn_output = tf.keras.activations.softmax(actor_layer)\n",
    "\n",
    "        # # Critic Model\n",
    "        # critic_layer = tf.keras.layers.Dense(units = 64)(common)\n",
    "        # critic_layer = tf.keras.layers.ReLU()(critic_layer)\n",
    "        # critic_nn_output = tf.keras.layers.Dense(units = 1)(critic_layer)\n",
    "\n",
    "        # # Combine into a model\n",
    "        # model = tf.keras.Model(name = name, inputs = nn_input, outputs = [actor_nn_output, critic_nn_output])\n",
    "\n",
    "        inputs = tf.keras.layers.Input(shape=self.state_size)\n",
    "#         x = tf.keras.layers.Dense(128, activation=\"relu\")(inputs)\n",
    "        common = tf.keras.layers.Dense(128, activation=\"relu\")(inputs)\n",
    "        action = tf.keras.layers.Dense(self.num_action, activation=\"softmax\")(common)\n",
    "        critic = tf.keras.layers.Dense(1)(common)\n",
    "\n",
    "        model = tf.keras.Model(inputs=inputs, outputs=[action, critic])\n",
    "\n",
    "        return model\n",
    "\n",
    "    def predict(self, state):\n",
    "        return self.model(tf.convert_to_tensor(state, self.data_type))\n",
    "    \n",
    "\n",
    "\n",
    "    def loss(self, action_probs, critic_values, rewards):\n",
    "        # Calculate accumulated reward Q(s, a) with discount\n",
    "        np_rewards = np.array(rewards)\n",
    "        num_reward = np_rewards.shape[0]\n",
    "        discounts = np.logspace(0, num_reward, base = self.reward_discount, num = num_reward)\n",
    "        \n",
    "        q_values = np.zeros(num_reward)\n",
    "        for i in range(num_reward):\n",
    "            q_values[i] = np.sum(np.multiply(np_rewards[i:], discounts[:num_reward - i]))\n",
    "        q_values = (q_values - np.mean(q_values)) / (np.std(q_values) + 1e-9)\n",
    "\n",
    "        # Calculate the Actor Loss and Advantage A(s, a) = Q_value(s, a) - value(s)\n",
    "        action_log_prbs = tf.math.log(action_probs)\n",
    "        advs = q_values - critic_values\n",
    "        actor_loss = -action_log_prbs * advs\n",
    "        \n",
    "        \n",
    "        # Calculate the critic loss \n",
    "        huber = tf.keras.losses.Huber()\n",
    "        critic_loss = huber(tf.convert_to_tensor(critic_values, dtype = self.data_type), tf.convert_to_tensor(q_values, dtype = self.data_type))\n",
    "\n",
    "        # Calculate the cross entropy of action distribution\n",
    "        entropy = tf.reduce_sum(action_probs * action_log_prbs * -1)\n",
    "        \n",
    "        # Compute loss as formular: loss = Sum of a trajectory(-log(Pr(s, a| Theta)) * Advantage + coefficient of value * Value - coefficient of entropy * cross entropy of action distribution)\n",
    "        # Advantage: A(s, a) = Q_value(s, a) - value(s)\n",
    "        # The modification refer to the implement of Baseline A2C from OpenAI\n",
    "        # Update model with a trajectory Every time.\n",
    "        return tf.reduce_sum(actor_loss + self.coef_value * critic_loss - self.coef_entropy * entropy)\n",
    "\n",
    "    def get_metrics_loss(self):\n",
    "        return self.avg_loss.result()\n",
    "    \n",
    "    def reset_metrics_loss(self):\n",
    "        self.avg_loss.reset_states()\n",
    "\n",
    "    def select_action(self, state):\n",
    "        # Predict the probability of each action(Stochastic Policy)\n",
    "        act_dist, value = self.predict([state])\n",
    "        act_dist = tf.squeeze(act_dist)\n",
    "        value = tf.squeeze(value)\n",
    "        # Assume using Epsilon Greedy Strategy\n",
    "        action = self.exploration_strategy.select_action()\n",
    "        \n",
    "        # If the index of action (return value) is -1, choose the action with highest probability that model predict\n",
    "#         if action == -1 or self.shutdown_explore == True:\n",
    "#             # Pick then action with HIGHTEST probability\n",
    "#             act_idx = tf.argmax(act_dist, axis = 0).numpy()\n",
    "#             return act_idx, act_dist, value\n",
    "#         else:\n",
    "#             # If the index of action (return value) is != -1, act randomly    \n",
    "#             return action, act_dist, value\n",
    "        return np.random.choice(self.num_action, p=np.squeeze(act_dist)), act_dist, value\n",
    "\n",
    "    def shutdown_explore(self):\n",
    "        self.is_shutdown_explore = True\n",
    "        self.exploration_strategy.shutdown_explore()\n",
    "    \n",
    "    def update(self, loss, tape):\n",
    "        gradients = tape.gradient(loss, self.model.trainable_variables)\n",
    "        # gradients = [gradients if gradients is not None else tf.zeros_like(var) for var, grad in zip(self.model.trainable_variables, gradients)]\n",
    "        self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))\n",
    "        self.avg_loss.update_state(loss)\n",
    "\n",
    "        # Update exploration rate of Epsilon Greedy Strategy\n",
    "        self.exploration_strategy.update_epsilon()\n",
    "\n",
    "        self.iter += 1\n",
    "        self.eps += 1\n",
    "\n",
    "    def train_on_env(self, env, is_show = False):\n",
    "        with tf.GradientTape() as tape:\n",
    "            tape.watch(self.model.trainable_variables)\n",
    "            episode_reward = 0\n",
    "            state = env.reset(is_show)\n",
    "\n",
    "            action_probs = []\n",
    "            critic_values = []\n",
    "            rewards = []\n",
    "\n",
    "            while not env.is_over():\n",
    "                # env.render()\n",
    "                action, act_prob_dist, value = self.select_action(state)\n",
    "                \n",
    "                act_prob = act_prob_dist[action]\n",
    "                state_prime, reward, is_done, info = env.act(action)\n",
    "                # print(f'State: {state}, Action: {action}, Reward: {reward}, State_Prime: {state_prime}')\n",
    "\n",
    "                state = state_prime\n",
    "                episode_reward += reward\n",
    "\n",
    "                action_probs.append(act_prob)\n",
    "                critic_values.append(value)\n",
    "                rewards.append(reward)\n",
    "\n",
    "            loss = self.loss(action_probs, critic_values, rewards)\n",
    "            self.update(loss, tape)\n",
    "            env.reset()\n",
    "\n",
    "            return episode_reward, loss"
   ]
  },
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   "outputs": [
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     "text": [
      "4 Physical GPUs, 1 Logical GPUs\n",
      "Episode 1\n"
     ]
    },
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 24 | Loss: 12.148900985717773\n",
      "Episode 21\n"
     ]
    },
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 46 | Loss: 18.8170223236084\n",
      "Episode 41\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 61 | Loss: 25.725399017333984\n",
      "Episode 61\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 102 | Loss: 31.720775604248047\n",
      "Episode 81\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 117 | Loss: 28.90985679626465\n",
      "Episode 101\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 120 | Loss: 18.084636688232422\n",
      "Episode 121\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 151 | Loss: 42.25358200073242\n",
      "Episode 141\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 172 | Loss: 26.66937255859375\n",
      "Episode 161\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 187 | Loss: 29.5415096282959\n",
      "Episode 181\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 196 | Loss: 48.2578125\n",
      "Episode 201\n"
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 185 | Loss: 52.57393264770508\n",
      "Episode 221\n"
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 151 | Loss: 18.620136260986328\n",
      "Episode 241\n"
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 187 | Loss: 35.090248107910156\n",
      "Episode 261\n"
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 192 | Loss: 38.935508728027344\n",
      "Episode 281\n"
     ]
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     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 48.818504333496094\n",
      "Episode 301\n"
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      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 49.27289581298828\n",
      "Episode 321\n"
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      "Episode 341\n"
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      "Avgerage Accumulated Reward: 187 | Loss: 51.05027389526367\n",
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      "Episode 1001\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 185 | Loss: 13.30035400390625\n",
      "Episode 1701\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 199 | Loss: 6.166097640991211\n",
      "Episode 1721\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 199 | Loss: 18.15808868408203\n",
      "Episode 1741\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 7.389034271240234\n",
      "Episode 1761\n"
     ]
    },
    {
     "data": {
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       "model_id": "6ad1a54529224bd1ab6911a7abfce975",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 199 | Loss: 4.621337413787842\n",
      "Episode 1781\n"
     ]
    },
    {
     "data": {
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       "model_id": "8d6faeb4a8cd44328c4868c458fabb37",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 199 | Loss: 23.726966857910156\n",
      "Episode 1801\n"
     ]
    },
    {
     "data": {
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       "model_id": "f782f96e4daa4dd485b21adab25827de",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 194 | Loss: 2.7167348861694336\n",
      "Episode 1821\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 27.235137939453125\n",
      "Episode 1841\n"
     ]
    },
    {
     "data": {
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       "model_id": "f5998dc7cd7c4cc5a10d4746392cb036",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 14.357011795043945\n",
      "Episode 1861\n"
     ]
    },
    {
     "data": {
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       "model_id": "bb42f80353c04d1f8fd7bf41c6692bd1",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 14.330015182495117\n",
      "Episode 1881\n"
     ]
    },
    {
     "data": {
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       "model_id": "c3f19b32228f4daebd5a602341163138",
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 50.10818099975586\n",
      "Episode 1901\n"
     ]
    },
    {
     "data": {
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       "model_id": "14a81508c4a74aceb12e7c175c791320",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 20.416004180908203\n",
      "Episode 1921\n"
     ]
    },
    {
     "data": {
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       "model_id": "c4226e9fea6247be8c85cacf8d5e0397",
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      "text/plain": [
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: -6.764279842376709\n",
      "Episode 1941\n"
     ]
    },
    {
     "data": {
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       "model_id": "4659d86e466e41969be9e614594c9dd9",
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       "HBox(children=(FloatProgress(value=0.0, max=20.0), HTML(value='')))"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 200 | Loss: 40.80251693725586\n",
      "Episode 1961\n"
     ]
    },
    {
     "data": {
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       "model_id": "ff0331e153584320ad0e7cd433dd6ad9",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Avgerage Accumulated Reward: 195 | Loss: 28.285842895507812\n",
      "Episode 1981\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accumulated Reward: 200 | Loss: 20.794048309326172\n",
      "Evaluate\n",
      "Accumulated Reward: 200.0\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1920x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CartPole-A2C Experiment\n",
    "# 2020/08/11 SYC \n",
    "\n",
    "# import models.A2C as A2C\n",
    "# import models.expStrategy.epsilonGreedy as EPSG\n",
    "# import envs.cartPole as cartPole\n",
    "import models.util as Util\n",
    "import logging\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import figure\n",
    "import numpy as np\n",
    "# To run tqdm on notebook, import tqdm.notebook\n",
    "from tqdm.notebook import tqdm\n",
    "# Run on pure python\n",
    "# from tqdm import tqdm\n",
    "\n",
    "# Config Logging format\n",
    "# logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO)\n",
    "# Config logging module to enable on notebook\n",
    "# logger = logging.getLogger()\n",
    "# logger.setLevel(logging.DEBUG)\n",
    "\n",
    "# Test GPU and show the available logical & physical GPUs\n",
    "Util.test_gpu()\n",
    "\n",
    "env = CartPoleEnv()\n",
    "NUM_STATE_FEATURES = env.get_num_state_features()\n",
    "NUM_ACTIONS = env.get_num_actions()\n",
    "EPISODE_NUM = 2000\n",
    "PRINT_EVERY_EPISODE = 20\n",
    "LEARNING_RATE = 0.003\n",
    "REWARD_DISCOUNT = 0.99\n",
    "\n",
    "exp_stg = EpsilonGreedy(0.2, NUM_ACTIONS)\n",
    "agent = Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT, LEARNING_RATE, exp_stg)\n",
    "\n",
    "state = env.reset()\n",
    "accum_reward = 0\n",
    "\n",
    "# tqdm progress bar\n",
    "bar = []\n",
    "# Reward & LossHistory\n",
    "r_his = []\n",
    "avg_r_his = [0]\n",
    "loss_his = []\n",
    "episode_reward = 0\n",
    "\n",
    "print(\"Episode 1\")\n",
    "for episode in range(1, EPISODE_NUM + 1):\n",
    "    if episode % PRINT_EVERY_EPISODE == 1:\n",
    "        if episode > 1:\n",
    "            bar.close()\n",
    "            print(\"Avgerage Accumulated Reward: {} | Loss: {}\".format(round(accum_reward / PRINT_EVERY_EPISODE), agent.get_metrics_loss()))\n",
    "            print(\"Episode {}\".format(episode))\n",
    "            agent.reset_metrics_loss()\n",
    "            avg_r_his.append(round(accum_reward / PRINT_EVERY_EPISODE))\n",
    "            accum_reward = 0\n",
    "        bar = tqdm(total = PRINT_EVERY_EPISODE)\n",
    "\n",
    "    episode_reward, episode_loss = agent.train_on_env(env)\n",
    "    accum_reward += episode_reward\n",
    "    r_his.append(episode_reward)\n",
    "    loss_his.append(episode_loss)\n",
    "    \n",
    "    episode_reward = 0\n",
    "\n",
    "    bar.update(1)        \n",
    "    env.reset()\n",
    "\n",
    "bar.close()    \n",
    "print(\"Accumulated Reward: {} | Loss: {}\".format(round(accum_reward / PRINT_EVERY_EPISODE), agent.get_metrics_loss()))\n",
    "avg_r_his.append(round(accum_reward / PRINT_EVERY_EPISODE))\n",
    "agent.reset_metrics_loss()\n",
    "\n",
    "# Evaluate the model\n",
    "agent.shutdown_explore()\n",
    "agent.reset_metrics_loss()\n",
    "# Reset Game\n",
    "env_state = env.reset()\n",
    "accum_reward = 0\n",
    "\n",
    "while not env.is_over():\n",
    "    # env.render()\n",
    "    action, act_log_prob, value = agent.select_action(state)\n",
    "    state_prime, reward, is_done, info = env.act(action)\n",
    "\n",
    "    state = state_prime\n",
    "    accum_reward += reward\n",
    "\n",
    "print(\"Evaluate\")\n",
    "print(\"Accumulated Reward: {}\".format(accum_reward))\n",
    "\n",
    "# Plot Reward History\n",
    "# figure(num=None, figsize=(24, 6), dpi=80)\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(24, 6), dpi=80)\n",
    "fig.suptitle(f'CartPole A2C Result (Evaluate Reward: {accum_reward})')\n",
    "x_datas = range(0, len(r_his))\n",
    "avg_x_datas = range(0, EPISODE_NUM + 1, PRINT_EVERY_EPISODE)\n",
    "\n",
    "ax1.plot(x_datas, r_his, color='blue')\n",
    "ax1.plot(avg_x_datas, avg_r_his, color='red')\n",
    "ax1.set_xlabel('Episodes')\n",
    "ax1.set_ylabel('Reward / Episode')\n",
    "ax1.grid()\n",
    "\n",
    "ax2.plot(x_datas, loss_his, color='orange')\n",
    "ax2.set_xlabel('Episodes')\n",
    "ax2.set_ylabel('Loss / Episode')\n",
    "ax2.grid()\n",
    "\n",
    "plt.savefig('CartPole-A2C-res.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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