# Use Advantage Actor-Critic to Play Acrobot-v1¶

TensorFlow version

In [1]:

    %matplotlib inline

    import sys
    import logging
    import itertools

    import numpy as np
    np.random.seed(0)
    import pandas as pd
    import gym
    import matplotlib.pyplot as plt
    import tensorflow.compat.v2 as tf
    tf.random.set_seed(0)
    from tensorflow import keras
    from tensorflow import nn
    from tensorflow import optimizers
    from tensorflow import losses
    from tensorflow.keras import layers
    from tensorflow.keras import models

    logging.basicConfig(level=logging.INFO,
            format='%(asctime)s [%(levelname)s] %(message)s',
            stream=sys.stdout, datefmt='%H:%M:%S')

In [2]:

    env = gym.make('Acrobot-v1')
    for key in vars(env):
        logging.info('%s: %s', key, vars(env)[key])
    for key in vars(env.spec):
        logging.info('%s: %s', key, vars(env.spec)[key])

    15:40:00 [INFO] env: <AcrobotEnv<Acrobot-v1>>
    15:40:00 [INFO] action_space: Discrete(3)
    15:40:00 [INFO] observation_space: Box(-28.274333953857422, 28.274333953857422, (6,), float32)
    15:40:00 [INFO] reward_range: (-inf, inf)
    15:40:00 [INFO] metadata: {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 15}
    15:40:00 [INFO] _max_episode_steps: 500
    15:40:00 [INFO] _elapsed_steps: None
    15:40:00 [INFO] id: Acrobot-v1
    15:40:00 [INFO] entry_point: gym.envs.classic_control:AcrobotEnv
    15:40:00 [INFO] reward_threshold: -100.0
    15:40:00 [INFO] nondeterministic: False
    15:40:00 [INFO] max_episode_steps: 500
    15:40:00 [INFO] _kwargs: {}
    15:40:00 [INFO] _env_name: Acrobot

In [3]:

    class AdvantageActorCriticAgent:
        def __init__(self, env):
            self.action_n = env.action_space.n
            self.gamma = 0.99

            self.actor_net = self.build_net(hidden_sizes=[100,],
                    output_size=self.action_n, output_activation=nn.softmax,
                    loss=losses.categorical_crossentropy,
                    learning_rate=0.0001)
            self.critic_net = self.build_net(hidden_sizes=[100,],
                    learning_rate=0.0002)

        def build_net(self, hidden_sizes, output_size=1,
                    activation=nn.relu, output_activation=None,
                    loss=losses.mse, learning_rate=0.001):
            model = keras.Sequential()
            for hidden_size in hidden_sizes:
                model.add(layers.Dense(units=hidden_size,
                        activation=activation))
            model.add(layers.Dense(units=output_size,
                    activation=output_activation))
            optimizer = optimizers.Adam(learning_rate)
            model.compile(optimizer=optimizer, loss=loss)
            return model

        def reset(self, mode=None):
            self.mode = mode
            if self.mode == 'train':
                self.trajectory = []
                self.discount = 1.

        def step(self, observation, reward, terminated):
            probs = self.actor_net.predict(observation[np.newaxis], verbose=0)[0]
            action = np.random.choice(self.action_n, p=probs)
            if self.mode == 'train':
                self.trajectory += [observation, reward, terminated, action]
                if len(self.trajectory) >= 8:
                    self.learn()
                self.discount *= self.gamma
            return action

        def close(self):
            pass

        def learn(self):
            state, _, _, action, next_state, reward, terminated, _ \
                    = self.trajectory[-8:]
            states = state[np.newaxis]
            v = self.critic_net.predict(states, verbose=0)
            next_v = self.critic_net.predict(next_state[np.newaxis], verbose=0)
            target = reward + (1. - terminated) * self.gamma * next_v
            td_error = target - v

            # update actor
            state_tensor = tf.convert_to_tensor(states, dtype=tf.float32)
            with tf.GradientTape() as tape:
                pi_tensor = self.actor_net(state_tensor)[0, action]
                logpi_tensor = tf.math.log(tf.clip_by_value(pi_tensor, 1e-6, 1.))
                loss_tensor = -self.discount * td_error * logpi_tensor
            grad_tensors = tape.gradient(loss_tensor, self.actor_net.variables)
            self.actor_net.optimizer.apply_gradients(zip(
                    grad_tensors, self.actor_net.variables))

            # update critic
            self.critic_net.fit(states, np.array([[target,],]), verbose=0)

    agent = AdvantageActorCriticAgent(env)

In [4]:

    def play_episode(env, agent, seed=None, mode=None, render=False):
        observation, _ = env.reset(seed=seed)
        reward, terminated, truncated = 0., False, False
        agent.reset(mode=mode)
        episode_reward, elapsed_steps = 0., 0
        while True:
            action = agent.step(observation, reward, terminated)
            if render:
                env.render()
            if terminated or truncated:
                break
            observation, reward, terminated, truncated, _ = env.step(action)
            episode_reward += reward
            elapsed_steps += 1
        agent.close()
        return episode_reward, elapsed_steps

    logging.info('==== train ====')
    episode_rewards = []
    for episode in itertools.count():
        episode_reward, elapsed_steps = play_episode(env, agent, seed=episode,
                mode='train')
        episode_rewards.append(episode_reward)
        logging.info('train episode %d: reward = %.2f, steps = %d',
                episode, episode_reward, elapsed_steps)
        if np.mean(episode_rewards[-10:]) > -120:
            break
    plt.plot(episode_rewards)

    logging.info('==== test ====')
    episode_rewards = []
    for episode in range(100):
        episode_reward, elapsed_steps = play_episode(env, agent)
        episode_rewards.append(episode_reward)
        logging.info('test episode %d: reward = %.2f, steps = %d',
                episode, episode_reward, elapsed_steps)
    logging.info('average episode reward = %.2f ± %.2f',
            np.mean(episode_rewards), np.std(episode_rewards))

    15:40:01 [INFO] ==== train ====
    15:43:01 [INFO] train episode 0: reward = -419.00, steps = 420
    15:46:29 [INFO] train episode 1: reward = -500.00, steps = 500
    15:49:50 [INFO] train episode 2: reward = -500.00, steps = 500
    15:53:12 [INFO] train episode 3: reward = -500.00, steps = 500
    15:56:33 [INFO] train episode 4: reward = -500.00, steps = 500
    15:59:55 [INFO] train episode 5: reward = -500.00, steps = 500
    16:03:19 [INFO] train episode 6: reward = -500.00, steps = 500
    16:06:40 [INFO] train episode 7: reward = -500.00, steps = 500
    16:09:58 [INFO] train episode 8: reward = -500.00, steps = 500
    16:13:06 [INFO] train episode 9: reward = -465.00, steps = 466
    16:14:25 [INFO] train episode 10: reward = -196.00, steps = 197
    16:15:41 [INFO] train episode 11: reward = -187.00, steps = 188
    16:16:57 [INFO] train episode 12: reward = -180.00, steps = 181
    16:18:31 [INFO] train episode 13: reward = -232.00, steps = 233
    16:19:40 [INFO] train episode 14: reward = -171.00, steps = 172
    16:20:35 [INFO] train episode 15: reward = -135.00, steps = 136
    16:21:33 [INFO] train episode 16: reward = -143.00, steps = 144
    16:22:42 [INFO] train episode 17: reward = -169.00, steps = 170
    16:23:32 [INFO] train episode 18: reward = -126.00, steps = 127
    16:25:01 [INFO] train episode 19: reward = -220.00, steps = 221
    16:26:04 [INFO] train episode 20: reward = -148.00, steps = 149
    16:27:35 [INFO] train episode 21: reward = -195.00, steps = 196
    16:28:35 [INFO] train episode 22: reward = -134.00, steps = 135
    16:29:40 [INFO] train episode 23: reward = -141.00, steps = 142
    16:31:33 [INFO] train episode 24: reward = -265.00, steps = 266
    16:32:40 [INFO] train episode 25: reward = -153.00, steps = 154
    16:33:43 [INFO] train episode 26: reward = -147.00, steps = 148
    16:34:39 [INFO] train episode 27: reward = -138.00, steps = 139
    16:35:39 [INFO] train episode 28: reward = -136.00, steps = 137
    16:36:39 [INFO] train episode 29: reward = -139.00, steps = 140
    16:37:38 [INFO] train episode 30: reward = -134.00, steps = 135
    16:39:33 [INFO] train episode 31: reward = -277.00, steps = 278
    16:40:25 [INFO] train episode 32: reward = -121.00, steps = 122
    16:41:22 [INFO] train episode 33: reward = -139.00, steps = 140
    16:42:11 [INFO] train episode 34: reward = -119.00, steps = 120
    16:43:08 [INFO] train episode 35: reward = -138.00, steps = 139
    16:44:06 [INFO] train episode 36: reward = -146.00, steps = 147
    16:44:51 [INFO] train episode 37: reward = -109.00, steps = 110
    16:45:45 [INFO] train episode 38: reward = -133.00, steps = 134
    16:46:32 [INFO] train episode 39: reward = -114.00, steps = 115
    16:47:16 [INFO] train episode 40: reward = -109.00, steps = 110
    16:48:03 [INFO] train episode 41: reward = -108.00, steps = 109
    16:48:46 [INFO] train episode 42: reward = -103.00, steps = 104
    16:49:35 [INFO] train episode 43: reward = -121.00, steps = 122
    16:50:15 [INFO] train episode 44: reward = -97.00, steps = 98
    16:50:15 [INFO] ==== test ====
    16:50:25 [INFO] test episode 0: reward = -110.00, steps = 111
    16:50:37 [INFO] test episode 1: reward = -123.00, steps = 124
    16:50:47 [INFO] test episode 2: reward = -104.00, steps = 105
    16:50:57 [INFO] test episode 3: reward = -95.00, steps = 96
    16:51:11 [INFO] test episode 4: reward = -136.00, steps = 137
    16:51:25 [INFO] test episode 5: reward = -143.00, steps = 144
    16:51:36 [INFO] test episode 6: reward = -114.00, steps = 115
    16:51:49 [INFO] test episode 7: reward = -135.00, steps = 136
    16:52:00 [INFO] test episode 8: reward = -122.00, steps = 123
    16:52:10 [INFO] test episode 9: reward = -104.00, steps = 105
    16:52:21 [INFO] test episode 10: reward = -112.00, steps = 113
    16:52:33 [INFO] test episode 11: reward = -132.00, steps = 133
    16:52:43 [INFO] test episode 12: reward = -97.00, steps = 98
    16:52:52 [INFO] test episode 13: reward = -98.00, steps = 99
    16:53:03 [INFO] test episode 14: reward = -121.00, steps = 122
    16:53:17 [INFO] test episode 15: reward = -163.00, steps = 164
    16:53:31 [INFO] test episode 16: reward = -152.00, steps = 153
    16:53:44 [INFO] test episode 17: reward = -132.00, steps = 133
    16:53:55 [INFO] test episode 18: reward = -115.00, steps = 116
    16:54:06 [INFO] test episode 19: reward = -120.00, steps = 121
    16:54:17 [INFO] test episode 20: reward = -125.00, steps = 126
    16:54:31 [INFO] test episode 21: reward = -154.00, steps = 155
    16:54:51 [INFO] test episode 22: reward = -184.00, steps = 185
    16:55:01 [INFO] test episode 23: reward = -99.00, steps = 100
    16:55:10 [INFO] test episode 24: reward = -92.00, steps = 93
    16:55:26 [INFO] test episode 25: reward = -174.00, steps = 175
    16:55:35 [INFO] test episode 26: reward = -101.00, steps = 102
    16:55:45 [INFO] test episode 27: reward = -99.00, steps = 100
    16:55:55 [INFO] test episode 28: reward = -114.00, steps = 115
    16:56:04 [INFO] test episode 29: reward = -86.00, steps = 87
    16:56:12 [INFO] test episode 30: reward = -90.00, steps = 91
    16:56:23 [INFO] test episode 31: reward = -112.00, steps = 113
    16:56:32 [INFO] test episode 32: reward = -98.00, steps = 99
    16:56:42 [INFO] test episode 33: reward = -104.00, steps = 105
    16:56:54 [INFO] test episode 34: reward = -131.00, steps = 132
    16:57:11 [INFO] test episode 35: reward = -168.00, steps = 169
    16:57:25 [INFO] test episode 36: reward = -136.00, steps = 137
    16:57:34 [INFO] test episode 37: reward = -97.00, steps = 98
    16:57:50 [INFO] test episode 38: reward = -178.00, steps = 179
    16:57:57 [INFO] test episode 39: reward = -80.00, steps = 81
    16:58:15 [INFO] test episode 40: reward = -194.00, steps = 195
    16:58:26 [INFO] test episode 41: reward = -119.00, steps = 120
    16:58:36 [INFO] test episode 42: reward = -119.00, steps = 120
    16:58:44 [INFO] test episode 43: reward = -91.00, steps = 92
    16:58:56 [INFO] test episode 44: reward = -125.00, steps = 126
    16:59:09 [INFO] test episode 45: reward = -145.00, steps = 146
    16:59:18 [INFO] test episode 46: reward = -98.00, steps = 99
    16:59:27 [INFO] test episode 47: reward = -105.00, steps = 106
    16:59:37 [INFO] test episode 48: reward = -103.00, steps = 104
    16:59:55 [INFO] test episode 49: reward = -204.00, steps = 205
    17:00:04 [INFO] test episode 50: reward = -98.00, steps = 99
    17:00:13 [INFO] test episode 51: reward = -107.00, steps = 108
    17:00:24 [INFO] test episode 52: reward = -117.00, steps = 118
    17:00:36 [INFO] test episode 53: reward = -136.00, steps = 137
    17:00:45 [INFO] test episode 54: reward = -104.00, steps = 105
    17:00:53 [INFO] test episode 55: reward = -93.00, steps = 94
    17:01:06 [INFO] test episode 56: reward = -135.00, steps = 136
    17:01:16 [INFO] test episode 57: reward = -113.00, steps = 114
    17:01:26 [INFO] test episode 58: reward = -120.00, steps = 121
    17:01:35 [INFO] test episode 59: reward = -106.00, steps = 107
    17:01:46 [INFO] test episode 60: reward = -117.00, steps = 118
    17:01:54 [INFO] test episode 61: reward = -93.00, steps = 94
    17:02:07 [INFO] test episode 62: reward = -149.00, steps = 150
    17:02:17 [INFO] test episode 63: reward = -117.00, steps = 118
    17:02:30 [INFO] test episode 64: reward = -144.00, steps = 145
    17:02:41 [INFO] test episode 65: reward = -129.00, steps = 130
    17:02:53 [INFO] test episode 66: reward = -134.00, steps = 135
    17:03:09 [INFO] test episode 67: reward = -175.00, steps = 176
    17:03:18 [INFO] test episode 68: reward = -108.00, steps = 109
    17:03:29 [INFO] test episode 69: reward = -118.00, steps = 119
    17:03:37 [INFO] test episode 70: reward = -98.00, steps = 99
    17:03:46 [INFO] test episode 71: reward = -101.00, steps = 102
    17:04:07 [INFO] test episode 72: reward = -228.00, steps = 229
    17:04:16 [INFO] test episode 73: reward = -107.00, steps = 108
    17:04:32 [INFO] test episode 74: reward = -173.00, steps = 174
    17:04:42 [INFO] test episode 75: reward = -106.00, steps = 107
    17:04:56 [INFO] test episode 76: reward = -143.00, steps = 144
    17:05:09 [INFO] test episode 77: reward = -131.00, steps = 132
    17:05:21 [INFO] test episode 78: reward = -111.00, steps = 112
    17:05:33 [INFO] test episode 79: reward = -127.00, steps = 128
    17:05:42 [INFO] test episode 80: reward = -103.00, steps = 104
    17:05:53 [INFO] test episode 81: reward = -112.00, steps = 113
    17:06:07 [INFO] test episode 82: reward = -150.00, steps = 151
    17:06:16 [INFO] test episode 83: reward = -101.00, steps = 102
    17:06:28 [INFO] test episode 84: reward = -132.00, steps = 133
    17:06:38 [INFO] test episode 85: reward = -99.00, steps = 100
    17:06:50 [INFO] test episode 86: reward = -111.00, steps = 112
    17:06:59 [INFO] test episode 87: reward = -92.00, steps = 93
    17:07:19 [INFO] test episode 88: reward = -210.00, steps = 211
    17:07:32 [INFO] test episode 89: reward = -131.00, steps = 132
    17:07:41 [INFO] test episode 90: reward = -99.00, steps = 100
    17:07:51 [INFO] test episode 91: reward = -115.00, steps = 116
    17:08:02 [INFO] test episode 92: reward = -102.00, steps = 103
    17:08:13 [INFO] test episode 93: reward = -101.00, steps = 102
    17:08:25 [INFO] test episode 94: reward = -108.00, steps = 109
    17:08:38 [INFO] test episode 95: reward = -137.00, steps = 138
    17:08:50 [INFO] test episode 96: reward = -134.00, steps = 135
    17:09:02 [INFO] test episode 97: reward = -128.00, steps = 129
    17:09:18 [INFO] test episode 98: reward = -188.00, steps = 189
    17:09:25 [INFO] test episode 99: reward = -83.00, steps = 84
    17:09:25 [INFO] average episode reward = -123.27 ± 29.22

In [5]:

    env.close()