Use Off-Policy VPG with Baseline to Play CartPole-v0¶

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.keras import layers
from tensorflow.keras import losses

logging.basicConfig(level=logging.INFO,
        format='%(asctime)s [%(levelname)s] %(message)s',
        stream=sys.stdout, datefmt='%H:%M:%S')
In [2]:
env = gym.make('CartPole-v0')
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])

22:32:33 [INFO] env: <CartPoleEnv<CartPole-v0>>
22:32:33 [INFO] action_space: Discrete(2)
22:32:33 [INFO] observation_space: Box(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)
22:32:33 [INFO] reward_range: (-inf, inf)
22:32:33 [INFO] metadata: {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
22:32:33 [INFO] _max_episode_steps: 200
22:32:33 [INFO] _elapsed_steps: None
22:32:33 [INFO] id: CartPole-v0
22:32:33 [INFO] entry_point: gym.envs.classic_control:CartPoleEnv
22:32:33 [INFO] reward_threshold: 195.0
22:32:33 [INFO] nondeterministic: False
22:32:33 [INFO] max_episode_steps: 200
22:32:33 [INFO] _kwargs: {}
22:32:33 [INFO] _env_name: CartPole
In [3]:
class OffPolicyVPGwBaselineAgent:
    def __init__(self, env):
        self.action_n = env.action_space.n
        self.gamma = 0.99

        def dot(y_true, y_pred):
            return -tf.reduce_sum(y_true * y_pred, axis=-1)

        self.policy_net = self.build_net(hidden_sizes=[],
                output_size=self.action_n,
                output_activation=nn.softmax,
                loss=dot, learning_rate=0.06)
        self.baseline_net = self.build_net(hidden_sizes=[],
                learning_rate=0.1)

    def build_net(self, hidden_sizes, output_size=1,
            activation=nn.relu, output_activation=None,
            use_bias=False, 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, use_bias=use_bias))
        model.add(layers.Dense(units=output_size,
                activation=output_activation, use_bias=use_bias))
        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 = []

    def step(self, observation, reward, terminated):
        if self.mode == 'train':
            action = np.random.choice(self.action_n)  # use random policy
            self.trajectory += [observation, reward, terminated, action]
        else:
            probs = self.policy_net.predict(observation[np.newaxis], verbose=0)[0]
            action = np.random.choice(self.action_n, p=probs)
        return action

    def close(self):
        if self.mode == 'train':
            self.learn()

    def learn(self):
        df = pd.DataFrame(np.array(self.trajectory, dtype=object).reshape(-1, 4),
                columns=['state', 'reward', 'terminated', 'action'])

        # update baseline
        df['discount'] = self.gamma ** df.index.to_series()
        df['discounted_reward'] = df['discount'] * df['reward'].astype(float)
        df['discounted_return'] = df['discounted_reward'][::-1].cumsum()
        df['return'] = df['discounted_return'] / df['discount']
        states = np.stack(df['state'])
        returns = df[['return',]].values
        self.baseline_net.fit(states, returns, verbose=0)

        # update policy
        states = np.stack(df['state'])
        df['baseline'] = self.baseline_net.predict(states, verbose=0)
        df['psi'] = df['discounted_return'] - df['baseline'] * df['discount']
        df['behavior_prob'] = 1. / self.action_n
        df['sample_weight'] = df['psi'] / df['behavior_prob']
        actions = np.eye(self.action_n)[df['action'].astype(int)]
        sample_weight = df[['sample_weight',]].values
        self.policy_net.fit(states, actions, sample_weight=sample_weight,
                verbose=0)


agent = OffPolicyVPGwBaselineAgent(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():
    play_episode(env, agent, seed=episode,
            mode='train')
    episode_reward, elapsed_steps = play_episode(env, agent)
    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:]) > 195:
        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))

22:32:35 [INFO] ==== train ====
22:32:35 [INFO] NumExpr defaulting to 8 threads.
22:32:58 [INFO] train episode 0: reward = 119.00, steps = 119
22:33:04 [INFO] train episode 1: reward = 31.00, steps = 31
22:33:13 [INFO] train episode 2: reward = 60.00, steps = 60
22:33:20 [INFO] train episode 3: reward = 40.00, steps = 40
22:33:27 [INFO] train episode 4: reward = 42.00, steps = 42
22:33:30 [INFO] train episode 5: reward = 15.00, steps = 15
22:33:35 [INFO] train episode 6: reward = 22.00, steps = 22
22:33:42 [INFO] train episode 7: reward = 38.00, steps = 38
22:33:50 [INFO] train episode 8: reward = 44.00, steps = 44
22:34:00 [INFO] train episode 9: reward = 62.00, steps = 62
22:34:19 [INFO] train episode 10: reward = 102.00, steps = 102
22:34:22 [INFO] train episode 11: reward = 16.00, steps = 16
22:34:41 [INFO] train episode 12: reward = 134.00, steps = 134
22:34:51 [INFO] train episode 13: reward = 59.00, steps = 59
22:35:07 [INFO] train episode 14: reward = 111.00, steps = 111
22:35:09 [INFO] train episode 15: reward = 16.00, steps = 16
22:35:13 [INFO] train episode 16: reward = 25.00, steps = 25
22:35:26 [INFO] train episode 17: reward = 88.00, steps = 88
22:35:36 [INFO] train episode 18: reward = 79.00, steps = 79
22:35:46 [INFO] train episode 19: reward = 59.00, steps = 59
22:35:54 [INFO] train episode 20: reward = 61.00, steps = 61
22:36:06 [INFO] train episode 21: reward = 82.00, steps = 82
22:36:11 [INFO] train episode 22: reward = 33.00, steps = 33
22:36:22 [INFO] train episode 23: reward = 76.00, steps = 76
22:36:27 [INFO] train episode 24: reward = 32.00, steps = 32
22:36:35 [INFO] train episode 25: reward = 58.00, steps = 58
22:36:44 [INFO] train episode 26: reward = 73.00, steps = 73
22:36:54 [INFO] train episode 27: reward = 75.00, steps = 75
22:36:59 [INFO] train episode 28: reward = 38.00, steps = 38
22:37:05 [INFO] train episode 29: reward = 50.00, steps = 50
22:37:15 [INFO] train episode 30: reward = 70.00, steps = 70
22:37:20 [INFO] train episode 31: reward = 38.00, steps = 38
22:37:30 [INFO] train episode 32: reward = 71.00, steps = 71
22:37:40 [INFO] train episode 33: reward = 63.00, steps = 63
22:37:45 [INFO] train episode 34: reward = 29.00, steps = 29
22:37:53 [INFO] train episode 35: reward = 41.00, steps = 41
22:38:05 [INFO] train episode 36: reward = 69.00, steps = 69
22:38:21 [INFO] train episode 37: reward = 96.00, steps = 96
22:38:36 [INFO] train episode 38: reward = 93.00, steps = 93
22:38:49 [INFO] train episode 39: reward = 100.00, steps = 100
22:39:00 [INFO] train episode 40: reward = 76.00, steps = 76
22:39:08 [INFO] train episode 41: reward = 47.00, steps = 47
22:39:17 [INFO] train episode 42: reward = 58.00, steps = 58
22:39:21 [INFO] train episode 43: reward = 31.00, steps = 31
22:39:29 [INFO] train episode 44: reward = 57.00, steps = 57
22:39:35 [INFO] train episode 45: reward = 41.00, steps = 41
22:39:49 [INFO] train episode 46: reward = 100.00, steps = 100
22:39:59 [INFO] train episode 47: reward = 74.00, steps = 74
22:40:05 [INFO] train episode 48: reward = 43.00, steps = 43
22:40:17 [INFO] train episode 49: reward = 86.00, steps = 86
22:40:24 [INFO] train episode 50: reward = 49.00, steps = 49
22:40:30 [INFO] train episode 51: reward = 40.00, steps = 40
22:40:39 [INFO] train episode 52: reward = 69.00, steps = 69
22:40:50 [INFO] train episode 53: reward = 73.00, steps = 73
22:40:56 [INFO] train episode 54: reward = 43.00, steps = 43
22:41:07 [INFO] train episode 55: reward = 75.00, steps = 75
22:41:13 [INFO] train episode 56: reward = 46.00, steps = 46
22:41:22 [INFO] train episode 57: reward = 57.00, steps = 57
22:41:27 [INFO] train episode 58: reward = 38.00, steps = 38
22:41:34 [INFO] train episode 59: reward = 54.00, steps = 54
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22:41:50 [INFO] train episode 61: reward = 61.00, steps = 61
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22:42:03 [INFO] train episode 63: reward = 30.00, steps = 30
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22:42:32 [INFO] train episode 66: reward = 104.00, steps = 104
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22:42:52 [INFO] train episode 68: reward = 114.00, steps = 114
22:42:59 [INFO] train episode 69: reward = 56.00, steps = 56
22:43:03 [INFO] train episode 70: reward = 34.00, steps = 34
22:43:12 [INFO] train episode 71: reward = 68.00, steps = 68
22:43:21 [INFO] train episode 72: reward = 74.00, steps = 74
22:43:26 [INFO] train episode 73: reward = 39.00, steps = 39
22:43:36 [INFO] train episode 74: reward = 80.00, steps = 80
22:43:45 [INFO] train episode 75: reward = 68.00, steps = 68
22:43:55 [INFO] train episode 76: reward = 83.00, steps = 83
22:44:06 [INFO] train episode 77: reward = 87.00, steps = 87
22:44:16 [INFO] train episode 78: reward = 78.00, steps = 78
22:44:21 [INFO] train episode 79: reward = 39.00, steps = 39
22:44:28 [INFO] train episode 80: reward = 54.00, steps = 54
22:44:35 [INFO] train episode 81: reward = 56.00, steps = 56
22:44:42 [INFO] train episode 82: reward = 50.00, steps = 50
22:44:54 [INFO] train episode 83: reward = 93.00, steps = 93
22:45:01 [INFO] train episode 84: reward = 55.00, steps = 55
22:45:08 [INFO] train episode 85: reward = 54.00, steps = 54
22:45:13 [INFO] train episode 86: reward = 41.00, steps = 41
22:45:21 [INFO] train episode 87: reward = 61.00, steps = 61
22:45:32 [INFO] train episode 88: reward = 88.00, steps = 88
22:45:43 [INFO] train episode 89: reward = 84.00, steps = 84
22:45:50 [INFO] train episode 90: reward = 51.00, steps = 51
22:45:58 [INFO] train episode 91: reward = 61.00, steps = 61
22:46:05 [INFO] train episode 92: reward = 55.00, steps = 55
22:46:14 [INFO] train episode 93: reward = 65.00, steps = 65
22:46:22 [INFO] train episode 94: reward = 56.00, steps = 56
22:46:35 [INFO] train episode 95: reward = 86.00, steps = 86
22:46:41 [INFO] train episode 96: reward = 34.00, steps = 34
22:46:48 [INFO] train episode 97: reward = 50.00, steps = 50
22:47:00 [INFO] train episode 98: reward = 92.00, steps = 92
22:47:08 [INFO] train episode 99: reward = 63.00, steps = 63
22:47:21 [INFO] train episode 100: reward = 97.00, steps = 97
22:47:31 [INFO] train episode 101: reward = 71.00, steps = 71
22:47:41 [INFO] train episode 102: reward = 64.00, steps = 64
22:47:53 [INFO] train episode 103: reward = 84.00, steps = 84
22:48:00 [INFO] train episode 104: reward = 54.00, steps = 54
22:48:06 [INFO] train episode 105: reward = 43.00, steps = 43
22:48:24 [INFO] train episode 106: reward = 144.00, steps = 144
22:48:33 [INFO] train episode 107: reward = 58.00, steps = 58
22:48:44 [INFO] train episode 108: reward = 79.00, steps = 79
22:48:51 [INFO] train episode 109: reward = 48.00, steps = 48
22:48:59 [INFO] train episode 110: reward = 56.00, steps = 56
22:49:11 [INFO] train episode 111: reward = 86.00, steps = 86
22:49:23 [INFO] train episode 112: reward = 92.00, steps = 92
22:49:33 [INFO] train episode 113: reward = 73.00, steps = 73
22:49:50 [INFO] train episode 114: reward = 126.00, steps = 126
22:49:59 [INFO] train episode 115: reward = 64.00, steps = 64
22:50:10 [INFO] train episode 116: reward = 77.00, steps = 77
22:50:25 [INFO] train episode 117: reward = 91.00, steps = 91
22:50:34 [INFO] train episode 118: reward = 66.00, steps = 66
22:50:49 [INFO] train episode 119: reward = 98.00, steps = 98
22:50:59 [INFO] train episode 120: reward = 48.00, steps = 48
22:51:11 [INFO] train episode 121: reward = 72.00, steps = 72
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22:51:34 [INFO] train episode 123: reward = 79.00, steps = 79
22:51:44 [INFO] train episode 124: reward = 62.00, steps = 62
22:51:54 [INFO] train episode 125: reward = 66.00, steps = 66
22:52:07 [INFO] train episode 126: reward = 106.00, steps = 106
22:52:17 [INFO] train episode 127: reward = 73.00, steps = 73
22:52:25 [INFO] train episode 128: reward = 58.00, steps = 58
22:52:32 [INFO] train episode 129: reward = 45.00, steps = 45
22:52:38 [INFO] train episode 130: reward = 42.00, steps = 42
22:52:51 [INFO] train episode 131: reward = 96.00, steps = 96
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22:53:07 [INFO] train episode 133: reward = 49.00, steps = 49
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22:53:36 [INFO] train episode 136: reward = 55.00, steps = 55
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22:57:51 [INFO] train episode 154: reward = 46.00, steps = 46
22:58:06 [INFO] train episode 155: reward = 82.00, steps = 82
22:58:29 [INFO] train episode 156: reward = 130.00, steps = 130
22:58:41 [INFO] train episode 157: reward = 63.00, steps = 63
22:58:56 [INFO] train episode 158: reward = 78.00, steps = 78
22:59:05 [INFO] train episode 159: reward = 57.00, steps = 57
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23:00:24 [INFO] train episode 164: reward = 177.00, steps = 177
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23:01:48 [INFO] train episode 173: reward = 59.00, steps = 59
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23:02:30 [INFO] train episode 177: reward = 120.00, steps = 120
23:02:38 [INFO] train episode 178: reward = 56.00, steps = 56
23:02:46 [INFO] train episode 179: reward = 59.00, steps = 59
23:03:03 [INFO] train episode 180: reward = 130.00, steps = 130
23:03:14 [INFO] train episode 181: reward = 92.00, steps = 92
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00:22:09 [INFO] train episode 614: reward = 102.00, steps = 102
00:22:18 [INFO] train episode 615: reward = 76.00, steps = 76
00:22:38 [INFO] train episode 616: reward = 162.00, steps = 162
00:22:47 [INFO] train episode 617: reward = 80.00, steps = 80
00:23:00 [INFO] train episode 618: reward = 137.00, steps = 137
00:23:14 [INFO] train episode 619: reward = 140.00, steps = 140
00:23:26 [INFO] train episode 620: reward = 124.00, steps = 124
00:23:39 [INFO] train episode 621: reward = 130.00, steps = 130
00:23:49 [INFO] train episode 622: reward = 101.00, steps = 101
00:24:07 [INFO] train episode 623: reward = 177.00, steps = 177
00:24:19 [INFO] train episode 624: reward = 119.00, steps = 119
00:24:27 [INFO] train episode 625: reward = 77.00, steps = 77
00:24:37 [INFO] train episode 626: reward = 101.00, steps = 101
00:24:50 [INFO] train episode 627: reward = 126.00, steps = 126
00:25:01 [INFO] train episode 628: reward = 105.00, steps = 105
00:25:14 [INFO] train episode 629: reward = 123.00, steps = 123
00:25:21 [INFO] train episode 630: reward = 72.00, steps = 72
00:25:42 [INFO] train episode 631: reward = 200.00, steps = 200
00:25:54 [INFO] train episode 632: reward = 118.00, steps = 118
00:26:05 [INFO] train episode 633: reward = 112.00, steps = 112
00:26:14 [INFO] train episode 634: reward = 92.00, steps = 92
00:26:32 [INFO] train episode 635: reward = 176.00, steps = 176
00:26:45 [INFO] train episode 636: reward = 132.00, steps = 132
00:26:55 [INFO] train episode 637: reward = 100.00, steps = 100
00:27:08 [INFO] train episode 638: reward = 130.00, steps = 130
00:27:17 [INFO] train episode 639: reward = 81.00, steps = 81
00:27:29 [INFO] train episode 640: reward = 124.00, steps = 124
00:27:39 [INFO] train episode 641: reward = 92.00, steps = 92
00:27:52 [INFO] train episode 642: reward = 138.00, steps = 138
00:28:05 [INFO] train episode 643: reward = 116.00, steps = 116
00:28:14 [INFO] train episode 644: reward = 96.00, steps = 96
00:28:23 [INFO] train episode 645: reward = 90.00, steps = 90
00:28:38 [INFO] train episode 646: reward = 141.00, steps = 141
00:28:46 [INFO] train episode 647: reward = 76.00, steps = 76
00:28:58 [INFO] train episode 648: reward = 125.00, steps = 125
00:29:08 [INFO] train episode 649: reward = 83.00, steps = 83
00:29:19 [INFO] train episode 650: reward = 102.00, steps = 102
00:29:33 [INFO] train episode 651: reward = 145.00, steps = 145
00:29:40 [INFO] train episode 652: reward = 68.00, steps = 68
00:29:52 [INFO] train episode 653: reward = 111.00, steps = 111
00:30:04 [INFO] train episode 654: reward = 120.00, steps = 120
00:30:15 [INFO] train episode 655: reward = 106.00, steps = 106
00:30:26 [INFO] train episode 656: reward = 106.00, steps = 106
00:30:36 [INFO] train episode 657: reward = 99.00, steps = 99
00:30:56 [INFO] train episode 658: reward = 200.00, steps = 200
00:31:05 [INFO] train episode 659: reward = 86.00, steps = 86
00:31:17 [INFO] train episode 660: reward = 120.00, steps = 120
00:31:27 [INFO] train episode 661: reward = 103.00, steps = 103
00:31:40 [INFO] train episode 662: reward = 128.00, steps = 128
00:31:52 [INFO] train episode 663: reward = 115.00, steps = 115
00:32:01 [INFO] train episode 664: reward = 88.00, steps = 88
00:32:11 [INFO] train episode 665: reward = 87.00, steps = 87
00:32:23 [INFO] train episode 666: reward = 128.00, steps = 128
00:32:33 [INFO] train episode 667: reward = 93.00, steps = 93
00:32:44 [INFO] train episode 668: reward = 104.00, steps = 104
00:33:02 [INFO] train episode 669: reward = 177.00, steps = 177
00:33:11 [INFO] train episode 670: reward = 91.00, steps = 91
00:33:20 [INFO] train episode 671: reward = 87.00, steps = 87
00:33:41 [INFO] train episode 672: reward = 200.00, steps = 200
00:33:54 [INFO] train episode 673: reward = 125.00, steps = 125
00:34:05 [INFO] train episode 674: reward = 113.00, steps = 113
00:34:13 [INFO] train episode 675: reward = 85.00, steps = 85
00:34:23 [INFO] train episode 676: reward = 89.00, steps = 89
00:34:33 [INFO] train episode 677: reward = 102.00, steps = 102
00:34:49 [INFO] train episode 678: reward = 154.00, steps = 154
00:35:05 [INFO] train episode 679: reward = 160.00, steps = 160
00:35:22 [INFO] train episode 680: reward = 177.00, steps = 177
00:35:35 [INFO] train episode 681: reward = 125.00, steps = 125
00:35:47 [INFO] train episode 682: reward = 111.00, steps = 111
00:35:57 [INFO] train episode 683: reward = 98.00, steps = 98
00:36:10 [INFO] train episode 684: reward = 127.00, steps = 127
00:36:20 [INFO] train episode 685: reward = 105.00, steps = 105
00:36:32 [INFO] train episode 686: reward = 116.00, steps = 116
00:36:50 [INFO] train episode 687: reward = 181.00, steps = 181
00:37:06 [INFO] train episode 688: reward = 154.00, steps = 154
00:37:20 [INFO] train episode 689: reward = 142.00, steps = 142
00:37:31 [INFO] train episode 690: reward = 104.00, steps = 104
00:37:46 [INFO] train episode 691: reward = 151.00, steps = 151
00:37:58 [INFO] train episode 692: reward = 121.00, steps = 121
00:38:06 [INFO] train episode 693: reward = 79.00, steps = 79
00:38:20 [INFO] train episode 694: reward = 145.00, steps = 145
00:38:34 [INFO] train episode 695: reward = 140.00, steps = 140
00:38:46 [INFO] train episode 696: reward = 120.00, steps = 120
00:38:57 [INFO] train episode 697: reward = 106.00, steps = 106
00:39:11 [INFO] train episode 698: reward = 144.00, steps = 144
00:39:26 [INFO] train episode 699: reward = 155.00, steps = 155
00:39:37 [INFO] train episode 700: reward = 101.00, steps = 101
00:39:48 [INFO] train episode 701: reward = 107.00, steps = 107
00:39:58 [INFO] train episode 702: reward = 101.00, steps = 101
00:40:13 [INFO] train episode 703: reward = 146.00, steps = 146
00:40:28 [INFO] train episode 704: reward = 156.00, steps = 156
00:40:40 [INFO] train episode 705: reward = 117.00, steps = 117
00:40:50 [INFO] train episode 706: reward = 102.00, steps = 102
00:41:04 [INFO] train episode 707: reward = 140.00, steps = 140
00:41:15 [INFO] train episode 708: reward = 106.00, steps = 106
00:41:26 [INFO] train episode 709: reward = 106.00, steps = 106
00:41:37 [INFO] train episode 710: reward = 106.00, steps = 106
00:41:49 [INFO] train episode 711: reward = 119.00, steps = 119
00:42:00 [INFO] train episode 712: reward = 113.00, steps = 113
00:42:20 [INFO] train episode 713: reward = 200.00, steps = 200
00:42:30 [INFO] train episode 714: reward = 104.00, steps = 104
00:42:43 [INFO] train episode 715: reward = 128.00, steps = 128
00:42:58 [INFO] train episode 716: reward = 144.00, steps = 144
00:43:14 [INFO] train episode 717: reward = 161.00, steps = 161
00:43:34 [INFO] train episode 718: reward = 200.00, steps = 200
00:43:52 [INFO] train episode 719: reward = 185.00, steps = 185
00:44:07 [INFO] train episode 720: reward = 157.00, steps = 157
00:44:23 [INFO] train episode 721: reward = 149.00, steps = 149
00:44:40 [INFO] train episode 722: reward = 173.00, steps = 173
00:45:01 [INFO] train episode 723: reward = 200.00, steps = 200
00:45:15 [INFO] train episode 724: reward = 135.00, steps = 135
00:45:34 [INFO] train episode 725: reward = 200.00, steps = 200
00:45:53 [INFO] train episode 726: reward = 195.00, steps = 195
00:46:13 [INFO] train episode 727: reward = 200.00, steps = 200
00:46:33 [INFO] train episode 728: reward = 200.00, steps = 200
00:46:53 [INFO] train episode 729: reward = 200.00, steps = 200
00:47:13 [INFO] train episode 730: reward = 200.00, steps = 200
00:47:33 [INFO] train episode 731: reward = 200.00, steps = 200
00:47:49 [INFO] train episode 732: reward = 157.00, steps = 157
00:48:09 [INFO] train episode 733: reward = 200.00, steps = 200
00:48:29 [INFO] train episode 734: reward = 200.00, steps = 200
00:48:29 [INFO] ==== test ====
00:48:43 [INFO] test episode 0: reward = 155.00, steps = 155
00:49:01 [INFO] test episode 1: reward = 199.00, steps = 199
00:49:16 [INFO] test episode 2: reward = 170.00, steps = 170
00:49:34 [INFO] test episode 3: reward = 200.00, steps = 200
00:49:51 [INFO] test episode 4: reward = 185.00, steps = 185
00:50:06 [INFO] test episode 5: reward = 166.00, steps = 166
00:50:20 [INFO] test episode 6: reward = 157.00, steps = 157
00:50:34 [INFO] test episode 7: reward = 158.00, steps = 158
00:50:47 [INFO] test episode 8: reward = 139.00, steps = 139
00:51:00 [INFO] test episode 9: reward = 149.00, steps = 149
00:51:14 [INFO] test episode 10: reward = 157.00, steps = 157
00:51:27 [INFO] test episode 11: reward = 142.00, steps = 142
00:51:44 [INFO] test episode 12: reward = 193.00, steps = 193
00:51:59 [INFO] test episode 13: reward = 164.00, steps = 164
00:52:17 [INFO] test episode 14: reward = 200.00, steps = 200
00:52:32 [INFO] test episode 15: reward = 163.00, steps = 163
00:52:50 [INFO] test episode 16: reward = 200.00, steps = 200
00:53:07 [INFO] test episode 17: reward = 193.00, steps = 193
00:53:26 [INFO] test episode 18: reward = 200.00, steps = 200
00:53:42 [INFO] test episode 19: reward = 180.00, steps = 180
00:54:00 [INFO] test episode 20: reward = 200.00, steps = 200
00:54:14 [INFO] test episode 21: reward = 158.00, steps = 158
00:54:33 [INFO] test episode 22: reward = 200.00, steps = 200
00:54:48 [INFO] test episode 23: reward = 173.00, steps = 173
00:55:02 [INFO] test episode 24: reward = 157.00, steps = 157
00:55:17 [INFO] test episode 25: reward = 166.00, steps = 166
00:55:35 [INFO] test episode 26: reward = 200.00, steps = 200
00:55:53 [INFO] test episode 27: reward = 200.00, steps = 200
00:56:06 [INFO] test episode 28: reward = 146.00, steps = 146
00:56:24 [INFO] test episode 29: reward = 200.00, steps = 200
00:56:42 [INFO] test episode 30: reward = 200.00, steps = 200
00:56:56 [INFO] test episode 31: reward = 156.00, steps = 156
00:57:14 [INFO] test episode 32: reward = 200.00, steps = 200
00:57:32 [INFO] test episode 33: reward = 200.00, steps = 200
00:57:50 [INFO] test episode 34: reward = 200.00, steps = 200
00:58:06 [INFO] test episode 35: reward = 172.00, steps = 172
00:58:22 [INFO] test episode 36: reward = 176.00, steps = 176
00:58:40 [INFO] test episode 37: reward = 200.00, steps = 200
00:58:52 [INFO] test episode 38: reward = 135.00, steps = 135
00:59:10 [INFO] test episode 39: reward = 200.00, steps = 200
00:59:25 [INFO] test episode 40: reward = 163.00, steps = 163
00:59:39 [INFO] test episode 41: reward = 158.00, steps = 158
00:59:51 [INFO] test episode 42: reward = 137.00, steps = 137
01:00:07 [INFO] test episode 43: reward = 168.00, steps = 168
01:00:24 [INFO] test episode 44: reward = 192.00, steps = 192
01:00:42 [INFO] test episode 45: reward = 199.00, steps = 199
01:00:58 [INFO] test episode 46: reward = 185.00, steps = 185
01:01:15 [INFO] test episode 47: reward = 187.00, steps = 187
01:01:30 [INFO] test episode 48: reward = 163.00, steps = 163
01:01:43 [INFO] test episode 49: reward = 135.00, steps = 135
01:01:59 [INFO] test episode 50: reward = 179.00, steps = 179
01:02:17 [INFO] test episode 51: reward = 200.00, steps = 200
01:02:35 [INFO] test episode 52: reward = 200.00, steps = 200
01:02:49 [INFO] test episode 53: reward = 149.00, steps = 149
01:03:01 [INFO] test episode 54: reward = 134.00, steps = 134
01:03:13 [INFO] test episode 55: reward = 138.00, steps = 138
01:03:32 [INFO] test episode 56: reward = 200.00, steps = 200
01:03:49 [INFO] test episode 57: reward = 200.00, steps = 200
01:04:05 [INFO] test episode 58: reward = 174.00, steps = 174
01:04:19 [INFO] test episode 59: reward = 152.00, steps = 152
01:04:32 [INFO] test episode 60: reward = 154.00, steps = 154
01:04:48 [INFO] test episode 61: reward = 165.00, steps = 165
01:05:04 [INFO] test episode 62: reward = 181.00, steps = 181
01:05:22 [INFO] test episode 63: reward = 200.00, steps = 200
01:05:40 [INFO] test episode 64: reward = 200.00, steps = 200
01:05:57 [INFO] test episode 65: reward = 200.00, steps = 200
01:06:15 [INFO] test episode 66: reward = 200.00, steps = 200
01:06:31 [INFO] test episode 67: reward = 169.00, steps = 169
01:06:48 [INFO] test episode 68: reward = 192.00, steps = 192
01:07:05 [INFO] test episode 69: reward = 187.00, steps = 187
01:07:21 [INFO] test episode 70: reward = 184.00, steps = 184
01:07:39 [INFO] test episode 71: reward = 200.00, steps = 200
01:07:52 [INFO] test episode 72: reward = 143.00, steps = 143
01:08:10 [INFO] test episode 73: reward = 193.00, steps = 193
01:08:24 [INFO] test episode 74: reward = 161.00, steps = 161
01:08:42 [INFO] test episode 75: reward = 200.00, steps = 200
01:09:01 [INFO] test episode 76: reward = 200.00, steps = 200
01:09:21 [INFO] test episode 77: reward = 200.00, steps = 200
01:09:35 [INFO] test episode 78: reward = 148.00, steps = 148
01:09:48 [INFO] test episode 79: reward = 146.00, steps = 146
01:10:06 [INFO] test episode 80: reward = 200.00, steps = 200
01:10:23 [INFO] test episode 81: reward = 190.00, steps = 190
01:10:37 [INFO] test episode 82: reward = 153.00, steps = 153
01:10:52 [INFO] test episode 83: reward = 164.00, steps = 164
01:11:10 [INFO] test episode 84: reward = 200.00, steps = 200
01:11:28 [INFO] test episode 85: reward = 199.00, steps = 199
01:11:46 [INFO] test episode 86: reward = 200.00, steps = 200
01:12:03 [INFO] test episode 87: reward = 187.00, steps = 187
01:12:21 [INFO] test episode 88: reward = 200.00, steps = 200
01:12:37 [INFO] test episode 89: reward = 181.00, steps = 181
01:12:53 [INFO] test episode 90: reward = 174.00, steps = 174
01:13:07 [INFO] test episode 91: reward = 148.00, steps = 148
01:13:24 [INFO] test episode 92: reward = 200.00, steps = 200
01:13:42 [INFO] test episode 93: reward = 200.00, steps = 200
01:14:00 [INFO] test episode 94: reward = 200.00, steps = 200
01:14:18 [INFO] test episode 95: reward = 195.00, steps = 195
01:14:35 [INFO] test episode 96: reward = 200.00, steps = 200
01:14:54 [INFO] test episode 97: reward = 200.00, steps = 200
01:15:12 [INFO] test episode 98: reward = 200.00, steps = 200
01:15:23 [INFO] test episode 99: reward = 120.00, steps = 120
01:15:23 [INFO] average episode reward = 178.56 ± 22.48
In [5]:
env.close()