Using Behavior Cloning to Play HumanoidBulletEnv-v0¶

TensorFlow version

In [1]:
import sys
import logging
import itertools
import time

import numpy as np
np.random.seed(0)
import pandas as pd
import gym
import pybullet_envs
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

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

Environment¶

In [2]:
env = gym.make('HumanoidBulletEnv-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])

00:09:50 [INFO] env: <HumanoidBulletEnv<HumanoidBulletEnv-v0>>
00:09:50 [INFO] action_space: Box(-1.0, 1.0, (17,), float32)
00:09:50 [INFO] observation_space: Box(-inf, inf, (44,), float32)
00:09:50 [INFO] reward_range: (-inf, inf)
00:09:50 [INFO] metadata: {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 60}
00:09:50 [INFO] _max_episode_steps: 1000
00:09:50 [INFO] _elapsed_steps: None
00:09:50 [INFO] id: HumanoidBulletEnv-v0
00:09:50 [INFO] entry_point: pybullet_envs.gym_locomotion_envs:HumanoidBulletEnv
00:09:50 [INFO] reward_threshold: None
00:09:50 [INFO] nondeterministic: False
00:09:50 [INFO] max_episode_steps: 1000
00:09:50 [INFO] _kwargs: {}
00:09:50 [INFO] _env_name: HumanoidBulletEnv

Agent¶

In [3]:
class ExpertAgent:
    def reset(self, mode):
        pass

    def step(self, observation, reward, terminated):
        weights0 = np.array([
                [+0.2289, +0.2584, +0.2595, +0.0173, +0.1293, -0.2980, +0.1410, +0.0982, +0.0216, +0.3724, +0.2204, -0.0734, -0.2420, -0.3443, -0.2738, -0.3825, +0.1504, -0.0930, -0.2680, +0.0685, +0.1592, +0.2534, -0.0787, -0.0426, +0.2591, +0.2134, -0.1631, +0.5168, +0.1444, +0.2736, +0.3623, -0.3472, +0.0393, -0.3056, +0.3850, -0.5231, +0.4511, +0.4223, -0.0905, +0.2265, +0.1662, +0.1092, +0.0426, -0.0209, +0.3260, -0.1788, +0.5045, -0.0254, +0.6684, +0.4659, +0.2193, +0.6121, -0.1771, +0.3024, +0.3233, -0.3380, +0.1834, +0.1947, +0.2840, -0.0212, +0.0610, +0.0254, -0.0687, +0.3342, -0.2010, +0.4851, -0.5739, -0.3228, -0.2242, +0.6149, +0.2704, -0.1006, +0.3950, -0.2684, +0.0090, -0.2419, +0.1112, -0.0795, +0.0021, -0.0317, +0.1345, -0.2847, +0.2323, +0.5374, -0.0119, -0.2098, +0.2074, +0.1693, +0.4537, -0.1453, +0.2661, -0.2997, +0.1043, -0.2340, +0.4472, -0.0415, +0.6437, +0.0279, +0.1609, +0.3353, -0.2240, -0.5433, -0.0053, +0.1863, +0.1038, +0.3337, +0.3889, +0.4159, -0.0836, -0.0826, +0.0872, -0.1362, +0.0061, -0.2982, -0.0074, -0.1452, -0.0655, -0.1369, -0.0493, -0.1082, -0.4080, +0.4732, +0.1229, +0.3087, -0.1222, +0.3846, +0.0719, -0.4536, +0.1202, -0.3903, -0.0445, +0.4052, +0.2922, +0.1095, +0.0317, +0.0974, +0.1149, -0.3794, +0.4364, -0.1597, +0.2889, +0.2431, +0.6867, +0.5117, -0.2517, +0.2835, -0.0365, +0.3303, +0.2569, +0.3636, -0.0945, +0.1853, -0.1341, +0.2595, -0.0174, -0.3227, +0.1999, +0.2348, +0.5826, -0.0480, +0.1449, +0.3866, +0.4740, +0.2486, +0.5011, -0.0334, +0.0953, +0.1072, -0.0901, -0.3235, +0.3258, +0.3003, +0.1229, -0.2257, -0.1920, -0.2025, -0.2022, +0.0843, +0.4563, -0.2654, +0.3158, -0.3102, +0.1105, -0.1171, +0.0465, -0.0692, +0.2824, +0.2598, -0.0289, -0.7774, +0.3501, -0.0635, +0.2257, -0.1644, +0.4091, -0.1967, +0.1614, +0.5468, +0.1365, +0.5054, +0.1986, +0.1368, +0.0886, -0.1305, +0.4297, +0.2482, -0.1914, -0.2909, +0.1784, +0.0690, +0.0224, +0.0378, +0.4991, -0.3410, +0.4281, -0.4574, +0.1952, -0.0444, -0.1939, +0.1637, +0.2385, +0.1354, +0.1817, -0.3849, +0.1909, +0.6585, +0.0997, +0.1376, +0.3598, +0.0611, +0.0124, -0.1492, -0.0659, -0.2671, -0.3925, +0.2333, +0.4184, -0.1364, -0.0361, -0.5937, -0.0327, -0.1142, -0.3334, +0.2963, +0.2350, -0.2600, +0.2784, -0.0720, -0.1594, -0.2652, +0.3922, +0.7291, -0.1830, +0.2253, +0.1965, +0.1972],
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                [-0.0193, -0.1029, +0.1858, +0.0139, +0.2608, -0.0146, +0.0154, +0.0489, -0.1093, +0.0104, -0.2219, +0.0648, -0.0656, -0.0877, +0.0203, -0.0899, -0.0903],
                [-0.0886, +0.1405, -0.3229, +0.1365, -0.0121, +0.0062, -0.0392, -0.0051, +0.1538, +0.2586, -0.0580, +0.1502, -0.0022, -0.0271, -0.0936, -0.4547, +0.0754],
                [+0.3475, +0.0164, -0.2164, +0.0795, -0.0293, +0.1575, +0.0395, -0.4194, +0.0051, -0.0399, +0.1034, +0.3149, -0.0858, -0.1606, -0.0197, -0.0434, -0.0950],
                [+0.1236, -0.0872, -0.2009, +0.3143, +0.1047, +0.0123, -0.1373, -0.0199, +0.0415, -0.0361, +0.4936, +0.1017, +0.0232, -0.2103, -0.0957, -0.1162, +0.0018],
                [-0.0303, +0.2271, +0.2671, -0.1139, +0.0505, +0.0459, +0.0181, -0.0978, +0.0442, +0.0428, -0.0411, +0.1537, -0.2310, -0.0133, +0.0393, +0.2811, +0.0302],
                [+0.0180, -0.2184, -0.0850, -0.1738, -0.1618, +0.0298, -0.0198, +0.3145, -0.1872, -0.0837, -0.0753, -0.1052, -0.0610, -0.1089, -0.0288, +0.0058, -0.0733],
                [+0.0005, +0.1507, +0.1555, +0.0516, -0.1457, -0.1738, -0.0570, +0.0876, +0.2975, +0.1416, +0.0454, -0.3029, +0.0148, -0.2664, +0.0913, +0.0374, +0.1604],
                [+0.0099, +0.1211, +0.3735, +0.2346, +0.0826, -0.0476, -0.0548, -0.0957, +0.0246, -0.0414, +0.0233, -0.0779, +0.0275, -0.1103, +0.1439, -0.1177, +0.0950],
                [+0.0991, -0.0663, -0.0226, -0.2599, -0.2676, -0.2060, -0.0898, +0.0218, -0.0629, +0.3991, -0.0857, -0.0919, -0.0442, +0.0097, -0.0634, -0.1714, +0.0120],
                [-0.0083, -0.2737, -0.0861, -0.1251, +0.0406, +0.0368, -0.1133, +0.1224, -0.1233, +0.0078, +0.1233, +0.0664, -0.2030, +0.0159, -0.4872, +0.0998, -0.1442],
                [-0.0557, +0.0044, +0.0650, -0.3940, +0.0112, +0.1438, +0.0244, -0.0566, -0.1197, +0.0153, -0.0472, -0.1162, -0.1096, -0.0067, +0.0748, -0.1948, +0.0299],
                [-0.1223, -0.1968, +0.0289, -0.1279, +0.2201, -0.2378, +0.0276, +0.2640, +0.0479, +0.0962, -0.0191, -0.2616, -0.1661, -0.1301, +0.1400, +0.0645, -0.1336],
                [+0.0914, +0.0268, +0.0498, +0.2482, -0.1405, -0.1721, +0.0066, +0.0427, +0.1062, -0.0699, +0.0238, +0.1893, +0.0670, -0.0401, -0.0636, +0.0611, -0.0596],
                [-0.1982, -0.2318, +0.1808, +0.0593, +0.1440, +0.3635, +0.0442, -0.1524, +0.0650, +0.0216, -0.0772, -0.1202, +0.2329, +0.0204, -0.0736, +0.2445, +0.0261],
                [-0.3588, +0.0298, -0.0034, -0.0458, +0.2777, +0.2485, +0.3801, -0.0695, -0.1679, -0.5362, -0.1887, +0.0389, +0.2512, -0.0413, +0.0365, -0.0915, +0.0519],
                [-0.0069, +0.1737, +0.1234, +0.0086, +0.0216, +0.1098, -0.0172, +0.0209, -0.1796, +0.1730, -0.0681, +0.1485, +0.1096, -0.0660, -0.0895, +0.1351, -0.0554],
                [-0.2458, +0.0149, +0.0155, -0.0099, +0.0206, +0.3378, +0.2285, +0.0385, -0.0532, +0.2575, -0.0545, +0.0151, +0.0540, -0.0833, +0.0946, +0.0446, -0.0862],
                [-0.1136, +0.1565, +0.1244, -0.0161, -0.0614, +0.1204, -0.0771, +0.2362, +0.0355, +0.2028, -0.0155, -0.0652, -0.2262, -0.0749, -0.1730, -0.0957, +0.2760],
                [+0.0634, -0.0011, -0.1770, -0.1426, +0.1599, +0.0892, -0.0261, +0.0790, +0.0812, +0.4281, +0.0143, +0.1554, +0.3770, -0.0206, -0.1564, +0.0818, -0.0514],
                [+0.0102, -0.1232, -0.2659, -0.1866, +0.0090, +0.2672, -0.0540, +0.1100, -0.2942, -0.2454, -0.0566, -0.2273, +0.1938, +0.0229, -0.1563, -0.0024, -0.0975],
                [-0.0645, -0.0004, +0.1548, +0.2129, -0.0429, -0.0628, +0.0306, +0.1769, -0.0491, -0.0285, -0.0621, -0.4293, +0.1821, +0.1154, -0.0027, +0.0264, -0.0107],
                [+0.3158, -0.0591, +0.0461, +0.0682, +0.0015, -0.0667, +0.1125, +0.0242, -0.0998, +0.0405, -0.1104, +0.0892, -0.1950, -0.0443, -0.1472, +0.1181, -0.0764],
                [-0.0697, -0.0853, +0.1715, +0.0623, +0.0484, -0.0205, -0.2043, -0.0463, -0.1115, +0.0058, -0.0556, -0.0808, +0.0454, -0.0959, +0.1666, +0.3646, +0.2677],
                [-0.1574, -0.2403, -0.1285, -0.0211, -0.2054, -0.0027, +0.1099, -0.1112, -0.2730, -0.1247, +0.1788, -0.1336, -0.1028, +0.1764, +0.2594, -0.0231, +0.1732],
                [+0.2124, +0.1167, -0.0895, -0.0324, +0.2043, +0.0680, -0.0162, -0.0062, -0.0897, +0.0854, +0.0748, +0.3693, -0.0414, +0.0972, +0.0094, +0.0354, +0.1289],
                [+0.2460, -0.1170, -0.1432, -0.0896, +0.0093, +0.0557, -0.0195, +0.0088, +0.0839, +0.0375, +0.1659, -0.0451, -0.1184, -0.2017, +0.0481, -0.1517, +0.1372],
                [-0.0602, +0.3316, -0.0452, -0.0173, +0.0148, +0.0459, +0.0167, -0.0842, +0.0428, +0.2873, -0.0527, +0.0752, -0.0441, +0.0583, +0.0443, +0.1112, -0.0272],
                [+0.0051, -0.0555, +0.1717, +0.1398, -0.1614, -0.0300, -0.1820, -0.0658, +0.0742, -0.0847, -0.1139, +0.0728, +0.1420, +0.2099, -0.2902, +0.1338, +0.1417],
                [-0.0094, -0.0020, -0.1126, -0.2572, -0.2766, +0.0049, +0.1369, +0.0054, -0.1328, +0.0361, +0.1361, +0.3090, +0.0047, +0.1695, +0.0896, -0.1901, -0.1786],
                [+0.1339, -0.0886, +0.0770, +0.0189, -0.1017, -0.0070, +0.1551, -0.0489, +0.0966, -0.0131, -0.0856, -0.1183, +0.1585, -0.0139, +0.1861, -0.1487, +0.0005],
                [+0.2260, +0.0098, -0.3146, -0.0441, +0.1948, +0.0520, +0.0863, -0.0021, -0.1183, +0.0328, +0.0124, +0.0056, +0.0944, -0.0319, +0.0679, +0.0774, +0.0409],
                [-0.0237, -0.3453, -0.1483, -0.2272, +0.1675, -0.1041, -0.0126, +0.3376, -0.0138, +0.1525, -0.0322, +0.2070, -0.2725, -0.0600, -0.1466, -0.2563, +0.0551],
                [+0.1966, -0.0838, -0.0537, +0.0933, -0.0060, -0.0721, -0.0399, -0.1153, -0.1987, -0.0413, +0.2588, +0.0648, -0.0473, +0.1387, +0.6180, +0.0229, -0.2610],
                [-0.1566, -0.0589, -0.0600, +0.0966, +0.0738, -0.0744, -0.0010, +0.0253, +0.2387, -0.0539, +0.0625, +0.1197, +0.0486, +0.0605, +0.2775, -0.0594, -0.0759],
                [-0.4288, -0.0693, +0.0067, +0.3567, +0.0813, -0.1369, -0.3431, +0.0559, -0.0320, +0.0158, -0.1947, -0.3235, +0.1294, -0.2915, -0.2301, -0.1980, +0.0892],
                [-0.1741, +0.0445, -0.0525, +0.0650, -0.0474, +0.1845, -0.0831, -0.0693, -0.0071, -0.0772, +0.0915, -0.0571, -0.1035, -0.0038, -0.1508, -0.2323, -0.1788],
                [+0.1543, -0.0994, +0.0674, +0.1953, +0.1254, -0.1263, +0.0854, -0.1048, +0.0106, -0.0203, +0.1019, -0.0881, +0.2788, -0.2237, -0.0723, +0.0393, -0.0969],
                [+0.0560, +0.2425, +0.1031, +0.1155, -0.1187, -0.1155, -0.0789, +0.2113, +0.0024, +0.2648, -0.1811, -0.0259, -0.1363, -0.2149, -0.2199, -0.1105, -0.1616],
                [-0.0967, +0.0007, -0.2230, +0.1197, +0.1085, +0.0267, -0.0633, +0.1611, -0.0078, +0.0088, -0.0879, -0.1362, +0.1364, -0.1784, +0.1725, -0.0491, -0.0177],
                [+0.0724, -0.0574, +0.0696, -0.0095, -0.1601, +0.1243, +0.2373, -0.1058, +0.1610, -0.1874, -0.0689, +0.1806, -0.2117, -0.0855, -0.0657, +0.0366, -0.0797],
                ])
        bias2 = np.array([-0.0356, +0.0776, -0.0344, +0.1375, +0.1048, +0.3648, +0.3240, +0.1319, +0.1161, +0.3373, +0.3193, +0.0120, +0.0253, -0.2434, -0.1291, +0.1042, -0.2448])

        hidden1 = (np.dot(observation, weights0) + bias0).clip(min=0)
        hidden2 = (np.dot(hidden1, weights1) + bias1).clip(min=0)
        action = np.dot(hidden2, weights2) + bias2
        return action

    def close(self):
        pass


expert_agent = ExpertAgent()
In [4]:
class SAReplayer:
    def __init__(self):
        self.fields = ['state', 'action']
        self.data = {field: [] for field in self.fields}
        self.memory = pd.DataFrame()

    def store(self, *args):
        for field, arg in zip(self.fields, args):
            self.data[field].append(arg)

    def sample(self, size=None):
        if len(self.memory) < len(self.data[self.fields[0]]):
            self.memory = pd.DataFrame(self.data, columns=self.fields)
        if size is None:
            indices = self.memory.index
        else:
            indices = np.random.choice(self.memory.index, size=size)
        return (np.stack(self.memory.loc[indices, field]) for field in
                self.fields)
In [5]:
class BCAgent:
    def __init__(self, env, expert_agent):
        self.expert_agent = expert_agent

        state_dim = env.observation_space.shape[0]
        action_dim = env.action_space.shape[0]

        self.net = self.build_net(input_size=state_dim,
                hidden_sizes=[256, 128], output_size=action_dim)

    def build_net(self, input_size=None, hidden_sizes=None, 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 == 'expert':
            self.expert_agent.reset(mode)
            self.expert_replayer = SAReplayer()

    def step(self, observation, reward, terminated):
        if self.mode == 'expert':
            action = expert_agent.step(observation, reward, terminated)
            self.expert_replayer.store(observation, action)
        else:
            action = self.net(observation[np.newaxis])[0]
        return action

    def close(self):
        if self.mode == 'expert':
            self.expert_agent.close()
            for _ in range(10):
                self.learn()

    def learn(self):
        states, actions = self.expert_replayer.sample(1024)
        self.net.fit(states, actions, verbose=0)


agent = BCAgent(env, expert_agent)
In [6]:
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('==== expert ====')
episode_rewards = []
for episode in range(100):
    episode_reward, elapsed_steps = play_episode(env, agent, mode='expert')
    episode_rewards.append(episode_reward)
    logging.info('expert episode %d: reward = %.2f, steps = %d',
            episode, episode_reward, elapsed_steps)
logging.info('average expert episode reward = %.2f ± %.2f',
        np.mean(episode_rewards), np.std(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))

00:09:52 [INFO] ==== expert ====
00:10:07 [INFO] expert episode 0: reward = 3636.76, steps = 1000
00:10:19 [INFO] expert episode 1: reward = 3584.22, steps = 1000
00:10:31 [INFO] expert episode 2: reward = 3582.76, steps = 1000
00:10:43 [INFO] expert episode 3: reward = 3641.40, steps = 1000
00:10:55 [INFO] expert episode 4: reward = 3581.67, steps = 1000
00:11:07 [INFO] expert episode 5: reward = 3597.90, steps = 1000
00:11:19 [INFO] expert episode 6: reward = 3576.58, steps = 1000
00:11:32 [INFO] expert episode 7: reward = 3627.58, steps = 1000
00:11:44 [INFO] expert episode 8: reward = 3585.69, steps = 1000
00:11:57 [INFO] expert episode 9: reward = 3612.16, steps = 1000
00:12:09 [INFO] expert episode 10: reward = 3534.30, steps = 1000
00:12:21 [INFO] expert episode 11: reward = 3621.15, steps = 1000
00:12:23 [INFO] expert episode 12: reward = 19.42, steps = 67
00:12:36 [INFO] expert episode 13: reward = 3595.52, steps = 1000
00:12:48 [INFO] expert episode 14: reward = 3612.40, steps = 1000
00:13:00 [INFO] expert episode 15: reward = 3607.04, steps = 1000
00:13:12 [INFO] expert episode 16: reward = 3626.01, steps = 1000
00:13:24 [INFO] expert episode 17: reward = 3628.98, steps = 1000
00:13:26 [INFO] expert episode 18: reward = -3.76, steps = 22
00:13:38 [INFO] expert episode 19: reward = 3621.28, steps = 1000
00:13:50 [INFO] expert episode 20: reward = 3562.74, steps = 1000
00:13:51 [INFO] expert episode 21: reward = -10.92, steps = 29
00:14:04 [INFO] expert episode 22: reward = 3626.82, steps = 1000
00:14:05 [INFO] expert episode 23: reward = 7.51, steps = 49
00:14:17 [INFO] expert episode 24: reward = 3637.81, steps = 1000
00:14:29 [INFO] expert episode 25: reward = 3594.70, steps = 1000
00:14:42 [INFO] expert episode 26: reward = 3583.24, steps = 1000
00:14:53 [INFO] expert episode 27: reward = 3637.90, steps = 1000
00:14:56 [INFO] expert episode 28: reward = 61.78, steps = 79
00:15:08 [INFO] expert episode 29: reward = 3604.58, steps = 1000
00:15:20 [INFO] expert episode 30: reward = 3611.99, steps = 1000
00:15:32 [INFO] expert episode 31: reward = 3638.35, steps = 1000
00:15:44 [INFO] expert episode 32: reward = 3584.59, steps = 1000
00:15:56 [INFO] expert episode 33: reward = 3612.45, steps = 1000
00:16:08 [INFO] expert episode 34: reward = 3639.87, steps = 1000
00:16:10 [INFO] expert episode 35: reward = -4.10, steps = 37
00:16:22 [INFO] expert episode 36: reward = 3610.86, steps = 1000
00:16:34 [INFO] expert episode 37: reward = 3627.18, steps = 1000
00:16:46 [INFO] expert episode 38: reward = 3625.77, steps = 1000
00:16:58 [INFO] expert episode 39: reward = 3602.48, steps = 1000
00:17:10 [INFO] expert episode 40: reward = 3513.75, steps = 1000
00:17:22 [INFO] expert episode 41: reward = 3616.72, steps = 1000
00:17:24 [INFO] expert episode 42: reward = 3.92, steps = 33
00:17:36 [INFO] expert episode 43: reward = 3610.82, steps = 1000
00:17:48 [INFO] expert episode 44: reward = 3590.80, steps = 1000
00:18:00 [INFO] expert episode 45: reward = 3598.02, steps = 1000
00:18:12 [INFO] expert episode 46: reward = 3584.06, steps = 1000
00:18:25 [INFO] expert episode 47: reward = 3631.53, steps = 1000
00:18:37 [INFO] expert episode 48: reward = 3635.42, steps = 1000
00:18:49 [INFO] expert episode 49: reward = 3614.20, steps = 1000
00:19:01 [INFO] expert episode 50: reward = 3616.14, steps = 1000
00:19:13 [INFO] expert episode 51: reward = 3561.36, steps = 1000
00:19:24 [INFO] expert episode 52: reward = 3597.01, steps = 1000
00:19:36 [INFO] expert episode 53: reward = 3599.24, steps = 1000
00:19:48 [INFO] expert episode 54: reward = 3619.03, steps = 1000
00:20:00 [INFO] expert episode 55: reward = 3627.83, steps = 1000
00:20:12 [INFO] expert episode 56: reward = 3626.63, steps = 1000
00:20:23 [INFO] expert episode 57: reward = 3595.42, steps = 1000
00:20:35 [INFO] expert episode 58: reward = 3608.29, steps = 1000
00:20:37 [INFO] expert episode 59: reward = 32.05, steps = 86
00:20:50 [INFO] expert episode 60: reward = 3555.76, steps = 1000
00:21:02 [INFO] expert episode 61: reward = 3608.93, steps = 1000
00:21:13 [INFO] expert episode 62: reward = 3618.58, steps = 1000
00:21:25 [INFO] expert episode 63: reward = 3628.57, steps = 1000
00:21:37 [INFO] expert episode 64: reward = 3638.96, steps = 1000
00:21:48 [INFO] expert episode 65: reward = 3617.05, steps = 1000
00:22:00 [INFO] expert episode 66: reward = 3598.20, steps = 1000
00:22:12 [INFO] expert episode 67: reward = 3445.20, steps = 1000
00:22:23 [INFO] expert episode 68: reward = 3611.77, steps = 1000
00:22:35 [INFO] expert episode 69: reward = 3607.09, steps = 1000
00:22:47 [INFO] expert episode 70: reward = 3621.62, steps = 1000
00:22:58 [INFO] expert episode 71: reward = 3602.90, steps = 1000
00:23:10 [INFO] expert episode 72: reward = 3530.39, steps = 1000
00:23:22 [INFO] expert episode 73: reward = 3603.60, steps = 1000
00:23:24 [INFO] expert episode 74: reward = 50.15, steps = 77
00:23:35 [INFO] expert episode 75: reward = 3592.32, steps = 1000
00:23:47 [INFO] expert episode 76: reward = 3620.81, steps = 1000
00:23:59 [INFO] expert episode 77: reward = 3611.51, steps = 1000
00:24:10 [INFO] expert episode 78: reward = 3599.40, steps = 1000
00:24:22 [INFO] expert episode 79: reward = 3632.42, steps = 1000
00:24:34 [INFO] expert episode 80: reward = 3602.78, steps = 1000
00:24:45 [INFO] expert episode 81: reward = 3646.05, steps = 1000
00:24:57 [INFO] expert episode 82: reward = 3609.66, steps = 1000
00:25:08 [INFO] expert episode 83: reward = 3606.29, steps = 1000
00:25:20 [INFO] expert episode 84: reward = 3601.71, steps = 1000
00:25:21 [INFO] expert episode 85: reward = -1.99, steps = 34
00:25:33 [INFO] expert episode 86: reward = 3595.97, steps = 1000
00:25:45 [INFO] expert episode 87: reward = 3620.52, steps = 1000
00:25:57 [INFO] expert episode 88: reward = 3601.69, steps = 1000
00:26:08 [INFO] expert episode 89: reward = 3594.18, steps = 1000
00:26:20 [INFO] expert episode 90: reward = 3460.20, steps = 1000
00:26:31 [INFO] expert episode 91: reward = 3619.25, steps = 1000
00:26:43 [INFO] expert episode 92: reward = 3619.31, steps = 1000
00:26:55 [INFO] expert episode 93: reward = 3417.55, steps = 1000
00:27:06 [INFO] expert episode 94: reward = 3615.89, steps = 1000
00:27:19 [INFO] expert episode 95: reward = 3591.82, steps = 1000
00:27:31 [INFO] expert episode 96: reward = 3623.42, steps = 1000
00:27:43 [INFO] expert episode 97: reward = 3614.98, steps = 1000
00:27:54 [INFO] expert episode 98: reward = 3581.44, steps = 1000
00:28:06 [INFO] expert episode 99: reward = 3579.60, steps = 1000
00:28:06 [INFO] average expert episode reward = 3242.22 ± 1076.24
00:28:06 [INFO] ==== test ====
00:28:19 [INFO] test episode 0: reward = 3591.33, steps = 1000
00:28:21 [INFO] test episode 1: reward = 274.67, steps = 124
00:28:22 [INFO] test episode 2: reward = 74.02, steps = 60
00:28:23 [INFO] test episode 3: reward = 169.85, steps = 87
00:28:24 [INFO] test episode 4: reward = 234.87, steps = 111
00:28:25 [INFO] test episode 5: reward = 58.16, steps = 43
00:28:27 [INFO] test episode 6: reward = 350.45, steps = 154
00:28:28 [INFO] test episode 7: reward = 149.80, steps = 84
00:28:29 [INFO] test episode 8: reward = 119.60, steps = 76
00:28:31 [INFO] test episode 9: reward = 292.74, steps = 132
00:28:32 [INFO] test episode 10: reward = 155.64, steps = 85
00:28:33 [INFO] test episode 11: reward = 90.13, steps = 72
00:28:34 [INFO] test episode 12: reward = 71.91, steps = 57
00:28:35 [INFO] test episode 13: reward = 30.54, steps = 48
00:28:35 [INFO] test episode 14: reward = -13.14, steps = 18
00:28:37 [INFO] test episode 15: reward = 227.48, steps = 115
00:28:38 [INFO] test episode 16: reward = 118.88, steps = 81
00:28:40 [INFO] test episode 17: reward = 113.25, steps = 81
00:28:41 [INFO] test episode 18: reward = 179.90, steps = 99
00:28:42 [INFO] test episode 19: reward = 97.85, steps = 74
00:28:44 [INFO] test episode 20: reward = 365.21, steps = 155
00:28:46 [INFO] test episode 21: reward = 147.99, steps = 93
00:28:48 [INFO] test episode 22: reward = 359.69, steps = 172
00:28:52 [INFO] test episode 23: reward = 625.16, steps = 258
00:28:53 [INFO] test episode 24: reward = 99.68, steps = 72
00:28:53 [INFO] test episode 25: reward = -6.33, steps = 24
00:29:06 [INFO] test episode 26: reward = 3618.06, steps = 1000
00:29:07 [INFO] test episode 27: reward = 96.28, steps = 64
00:29:08 [INFO] test episode 28: reward = 124.48, steps = 70
00:29:22 [INFO] test episode 29: reward = 3614.14, steps = 1000
00:29:22 [INFO] test episode 30: reward = 46.95, steps = 43
00:29:24 [INFO] test episode 31: reward = 132.25, steps = 95
00:29:26 [INFO] test episode 32: reward = 499.32, steps = 195
00:29:28 [INFO] test episode 33: reward = 380.34, steps = 158
00:29:29 [INFO] test episode 34: reward = 84.36, steps = 59
00:29:32 [INFO] test episode 35: reward = 404.09, steps = 174
00:29:33 [INFO] test episode 36: reward = 128.64, steps = 80
00:29:39 [INFO] test episode 37: reward = 1493.17, steps = 505
00:29:40 [INFO] test episode 38: reward = 73.96, steps = 53
00:29:41 [INFO] test episode 39: reward = 105.85, steps = 88
00:29:42 [INFO] test episode 40: reward = 125.77, steps = 79
00:29:43 [INFO] test episode 41: reward = 68.77, steps = 47
00:29:44 [INFO] test episode 42: reward = 108.92, steps = 72
00:29:50 [INFO] test episode 43: reward = 1190.28, steps = 423
00:29:50 [INFO] test episode 44: reward = 63.65, steps = 62
00:29:53 [INFO] test episode 45: reward = 422.49, steps = 172
00:29:54 [INFO] test episode 46: reward = 154.40, steps = 94
00:29:55 [INFO] test episode 47: reward = 147.87, steps = 100
00:29:56 [INFO] test episode 48: reward = 41.67, steps = 50
00:29:57 [INFO] test episode 49: reward = 75.09, steps = 65
00:29:59 [INFO] test episode 50: reward = 246.53, steps = 133
00:30:01 [INFO] test episode 51: reward = 345.61, steps = 156
00:30:02 [INFO] test episode 52: reward = 108.76, steps = 74
00:30:03 [INFO] test episode 53: reward = 158.85, steps = 91
00:30:04 [INFO] test episode 54: reward = 31.78, steps = 56
00:30:05 [INFO] test episode 55: reward = 150.05, steps = 83
00:30:08 [INFO] test episode 56: reward = 467.94, steps = 193
00:30:09 [INFO] test episode 57: reward = 102.30, steps = 84
00:30:10 [INFO] test episode 58: reward = 144.92, steps = 81
00:30:11 [INFO] test episode 59: reward = 68.53, steps = 67
00:30:14 [INFO] test episode 60: reward = 498.84, steps = 211
00:30:15 [INFO] test episode 61: reward = 182.17, steps = 105
00:30:19 [INFO] test episode 62: reward = 693.49, steps = 267
00:30:20 [INFO] test episode 63: reward = 69.82, steps = 78
00:30:22 [INFO] test episode 64: reward = 443.87, steps = 179
00:30:23 [INFO] test episode 65: reward = 143.08, steps = 98
00:30:26 [INFO] test episode 66: reward = 484.11, steps = 191
00:30:27 [INFO] test episode 67: reward = 152.25, steps = 87
00:30:28 [INFO] test episode 68: reward = 113.86, steps = 75
00:30:29 [INFO] test episode 69: reward = 47.38, steps = 52
00:30:30 [INFO] test episode 70: reward = 160.67, steps = 83
00:30:31 [INFO] test episode 71: reward = 88.66, steps = 66
00:30:32 [INFO] test episode 72: reward = 112.63, steps = 77
00:30:34 [INFO] test episode 73: reward = 310.11, steps = 144
00:30:35 [INFO] test episode 74: reward = 79.71, steps = 82
00:30:36 [INFO] test episode 75: reward = 84.37, steps = 81
00:30:49 [INFO] test episode 76: reward = 3588.07, steps = 1000
00:30:51 [INFO] test episode 77: reward = 147.01, steps = 98
00:30:51 [INFO] test episode 78: reward = 89.59, steps = 59
00:30:52 [INFO] test episode 79: reward = 73.18, steps = 53
00:30:53 [INFO] test episode 80: reward = 80.16, steps = 70
00:30:55 [INFO] test episode 81: reward = 281.17, steps = 139
00:30:56 [INFO] test episode 82: reward = 84.49, steps = 64
00:31:09 [INFO] test episode 83: reward = 3625.25, steps = 1000
00:31:10 [INFO] test episode 84: reward = 89.55, steps = 73
00:31:12 [INFO] test episode 85: reward = 247.93, steps = 139
00:31:14 [INFO] test episode 86: reward = 384.44, steps = 160
00:31:16 [INFO] test episode 87: reward = 219.08, steps = 111
00:31:18 [INFO] test episode 88: reward = 452.34, steps = 188
00:31:20 [INFO] test episode 89: reward = 386.57, steps = 163
00:31:21 [INFO] test episode 90: reward = 129.46, steps = 85
00:31:22 [INFO] test episode 91: reward = 32.67, steps = 49
00:31:24 [INFO] test episode 92: reward = 313.64, steps = 135
00:31:25 [INFO] test episode 93: reward = 96.43, steps = 73
00:31:26 [INFO] test episode 94: reward = 103.55, steps = 92
00:31:27 [INFO] test episode 95: reward = 214.60, steps = 108
00:31:30 [INFO] test episode 96: reward = 503.47, steps = 205
00:31:31 [INFO] test episode 97: reward = 199.30, steps = 104
00:31:32 [INFO] test episode 98: reward = 125.53, steps = 80
00:31:33 [INFO] test episode 99: reward = 125.81, steps = 72
00:31:33 [INFO] average episode reward = 382.64 ± 770.74
In [7]:
env.close()