Use Closed-Form Policy to Play Taxi-v3¶

In [1]:
import sys
import logging
import itertools

import numpy as np
np.random.seed(0)
import gym

logging.basicConfig(level=logging.INFO,
        format='%(asctime)s [%(levelname)s] %(message)s',
        stream=sys.stdout, datefmt='%H:%M:%S')
In [2]:
env = gym.make('Taxi-v3')
for key in vars(env.spec):
    logging.info('%s: %s', key, vars(env.spec)[key])
for key in vars(env.unwrapped):
    logging.info('%s: %s', key, vars(env.unwrapped)[key])

00:00:00 [INFO] id: Taxi-v3
00:00:00 [INFO] entry_point: gym.envs.toy_text:TaxiEnv
00:00:00 [INFO] reward_threshold: 8
00:00:00 [INFO] nondeterministic: False
00:00:00 [INFO] max_episode_steps: 200
00:00:00 [INFO] order_enforce: True
00:00:00 [INFO] _kwargs: {}
00:00:00 [INFO] _env_name: Taxi
00:00:00 [INFO] desc: [[b'+' b'-' b'-' b'-' b'-' b'-' b'-' b'-' b'-' b'-' b'+']
 [b'|' b'R' b':' b' ' b'|' b' ' b':' b' ' b':' b'G' b'|']
 [b'|' b' ' b':' b' ' b'|' b' ' b':' b' ' b':' b' ' b'|']
 [b'|' b' ' b':' b' ' b':' b' ' b':' b' ' b':' b' ' b'|']
 [b'|' b' ' b'|' b' ' b':' b' ' b'|' b' ' b':' b' ' b'|']
 [b'|' b'Y' b'|' b' ' b':' b' ' b'|' b'B' b':' b' ' b'|']
 [b'+' b'-' b'-' b'-' b'-' b'-' b'-' b'-' b'-' b'-' b'+']]
00:00:00 [INFO] locs: [(0, 0), (0, 4), (4, 0), (4, 3)]
00:00:00 [INFO] P: {0: {0: [(1.0, 100, -1, False)], 1: [(1.0, 0, -1, False)], 2: [(1.0, 20, -1, False)], 3: [(1.0, 0, -1, False)], 4: [(1.0, 16, -1, False)], 5: [(1.0, 0, -10, False)]}, 1: {0: [(1.0, 101, -1, False)], 1: [(1.0, 1, -1, False)], 2: [(1.0, 21, -1, False)], 3: [(1.0, 1, -1, False)], 4: [(1.0, 17, -1, False)], 5: [(1.0, 1, -10, False)]}, 2: {0: [(1.0, 102, -1, False)], 1: [(1.0, 2, -1, False)], 2: [(1.0, 22, -1, False)], 3: [(1.0, 2, -1, False)], 4: [(1.0, 18, -1, False)], 5: [(1.0, 2, -10, False)]}, 3: {0: [(1.0, 103, -1, False)], 1: [(1.0, 3, -1, False)], 2: [(1.0, 23, -1, False)], 3: [(1.0, 3, -1, False)], 4: [(1.0, 19, -1, False)], 5: [(1.0, 3, -10, False)]}, 4: {0: [(1.0, 104, -1, False)], 1: [(1.0, 4, -1, False)], 2: [(1.0, 24, -1, False)], 3: [(1.0, 4, -1, False)], 4: [(1.0, 4, -10, False)], 5: [(1.0, 4, -10, False)]}, 5: {0: [(1.0, 105, -1, False)], 1: [(1.0, 5, -1, False)], 2: [(1.0, 25, -1, False)], 3: [(1.0, 5, -1, False)], 4: [(1.0, 5, -10, False)], 5: [(1.0, 5, -10, False)]}, 6: {0: [(1.0, 106, -1, False)], 1: [(1.0, 6, -1, False)], 2: [(1.0, 26, -1, False)], 3: [(1.0, 6, -1, False)], 4: [(1.0, 6, -10, False)], 5: [(1.0, 6, -10, False)]}, 7: {0: [(1.0, 107, -1, False)], 1: [(1.0, 7, -1, False)], 2: [(1.0, 27, -1, False)], 3: [(1.0, 7, -1, False)], 4: [(1.0, 7, -10, False)], 5: [(1.0, 7, -10, False)]}, 8: {0: [(1.0, 108, -1, False)], 1: [(1.0, 8, -1, False)], 2: [(1.0, 28, -1, False)], 3: [(1.0, 8, -1, False)], 4: [(1.0, 8, -10, False)], 5: [(1.0, 8, -10, False)]}, 9: {0: [(1.0, 109, -1, False)], 1: [(1.0, 9, -1, False)], 2: [(1.0, 29, -1, False)], 3: [(1.0, 9, -1, False)], 4: [(1.0, 9, -10, False)], 5: [(1.0, 9, -10, False)]}, 10: {0: [(1.0, 110, -1, False)], 1: [(1.0, 10, -1, False)], 2: [(1.0, 30, -1, False)], 3: [(1.0, 10, -1, False)], 4: [(1.0, 10, -10, False)], 5: [(1.0, 10, -10, False)]}, 11: {0: [(1.0, 111, -1, False)], 1: [(1.0, 11, -1, False)], 2: [(1.0, 31, -1, False)], 3: [(1.0, 11, -1, False)], 4: [(1.0, 11, -10, False)], 5: [(1.0, 11, -10, False)]}, 12: {0: [(1.0, 112, -1, False)], 1: [(1.0, 12, -1, False)], 2: [(1.0, 32, -1, False)], 3: [(1.0, 12, -1, False)], 4: [(1.0, 12, -10, False)], 5: [(1.0, 12, -10, False)]}, 13: {0: [(1.0, 113, -1, False)], 1: [(1.0, 13, -1, False)], 2: [(1.0, 33, -1, False)], 3: [(1.0, 13, -1, False)], 4: [(1.0, 13, -10, False)], 5: [(1.0, 13, -10, False)]}, 14: {0: [(1.0, 114, -1, False)], 1: [(1.0, 14, -1, False)], 2: [(1.0, 34, -1, False)], 3: [(1.0, 14, -1, False)], 4: [(1.0, 14, -10, False)], 5: [(1.0, 14, -10, False)]}, 15: {0: [(1.0, 115, -1, False)], 1: [(1.0, 15, -1, False)], 2: [(1.0, 35, -1, False)], 3: [(1.0, 15, -1, False)], 4: [(1.0, 15, -10, False)], 5: [(1.0, 15, -10, False)]}, 16: {0: [(1.0, 116, -1, False)], 1: [(1.0, 16, -1, False)], 2: [(1.0, 36, -1, False)], 3: [(1.0, 16, -1, False)], 4: [(1.0, 16, -10, False)], 5: [(1.0, 0, 20, True)]}, 17: {0: [(1.0, 117, -1, False)], 1: [(1.0, 17, -1, False)], 2: [(1.0, 37, -1, False)], 3: [(1.0, 17, -1, False)], 4: [(1.0, 17, -10, False)], 5: [(1.0, 1, -1, False)]}, 18: {0: [(1.0, 118, -1, False)], 1: [(1.0, 18, -1, False)], 2: [(1.0, 38, -1, False)], 3: [(1.0, 18, -1, False)], 4: [(1.0, 18, -10, False)], 5: [(1.0, 2, -1, False)]}, 19: {0: [(1.0, 119, -1, False)], 1: [(1.0, 19, -1, False)], 2: [(1.0, 39, -1, False)], 3: [(1.0, 19, -1, False)], 4: [(1.0, 19, -10, False)], 5: [(1.0, 3, -1, False)]}, 20: {0: [(1.0, 120, -1, False)], 1: [(1.0, 20, -1, False)], 2: [(1.0, 20, -1, False)], 3: [(1.0, 0, -1, False)], 4: [(1.0, 20, -10, False)], 5: [(1.0, 20, -10, False)]}, 21: {0: [(1.0, 121, -1, False)], 1: [(1.0, 21, -1, False)], 2: [(1.0, 21, -1, False)], 3: [(1.0, 1, -1, False)], 4: [(1.0, 21, -10, False)], 5: [(1.0, 21, -10, False)]}, 22: {0: [(1.0, 122, -1, False)], 1: [(1.0, 22, -1, False)], 2: [(1.0, 22, -1, False)], 3: [(1.0, 2, -1, False)], 4: [(1.0, 22, -10, False)], 5: [(1.0, 22, -10, False)]}, 23: {0: [(1.0, 123, -1, False)], 1: [(1.0, 23, -1, False)], 2: [(1.0, 23, -1, False)], 3: [(1.0, 3, 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False)], 3: [(1.0, 9, -1, False)], 4: [(1.0, 29, -10, False)], 5: [(1.0, 29, -10, False)]}, 30: {0: [(1.0, 130, -1, False)], 1: [(1.0, 30, -1, False)], 2: [(1.0, 30, -1, False)], 3: [(1.0, 10, -1, False)], 4: [(1.0, 30, -10, False)], 5: [(1.0, 30, -10, False)]}, 31: {0: [(1.0, 131, -1, False)], 1: [(1.0, 31, -1, False)], 2: [(1.0, 31, -1, False)], 3: [(1.0, 11, -1, False)], 4: [(1.0, 31, -10, False)], 5: [(1.0, 31, -10, False)]}, 32: {0: [(1.0, 132, -1, False)], 1: [(1.0, 32, -1, False)], 2: [(1.0, 32, -1, False)], 3: [(1.0, 12, -1, False)], 4: [(1.0, 32, -10, False)], 5: [(1.0, 32, -10, False)]}, 33: {0: [(1.0, 133, -1, False)], 1: [(1.0, 33, -1, False)], 2: [(1.0, 33, -1, False)], 3: [(1.0, 13, -1, False)], 4: [(1.0, 33, -10, False)], 5: [(1.0, 33, -10, False)]}, 34: {0: [(1.0, 134, -1, False)], 1: [(1.0, 34, -1, False)], 2: [(1.0, 34, -1, False)], 3: [(1.0, 14, -1, False)], 4: [(1.0, 34, -10, False)], 5: [(1.0, 34, -10, False)]}, 35: {0: [(1.0, 135, -1, False)], 1: [(1.0, 35, -1, False)], 2: [(1.0, 35, -1, False)], 3: [(1.0, 15, -1, False)], 4: [(1.0, 35, -10, False)], 5: [(1.0, 35, -10, False)]}, 36: {0: [(1.0, 136, -1, False)], 1: [(1.0, 36, -1, False)], 2: [(1.0, 36, -1, False)], 3: [(1.0, 16, -1, False)], 4: [(1.0, 36, -10, False)], 5: [(1.0, 36, -10, False)]}, 37: {0: [(1.0, 137, -1, False)], 1: [(1.0, 37, -1, False)], 2: [(1.0, 37, -1, False)], 3: [(1.0, 17, -1, False)], 4: [(1.0, 37, -10, False)], 5: [(1.0, 37, -10, False)]}, 38: {0: [(1.0, 138, -1, False)], 1: [(1.0, 38, -1, False)], 2: [(1.0, 38, -1, False)], 3: [(1.0, 18, -1, False)], 4: [(1.0, 38, -10, False)], 5: [(1.0, 38, -10, False)]}, 39: {0: [(1.0, 139, -1, False)], 1: [(1.0, 39, -1, False)], 2: [(1.0, 39, -1, False)], 3: [(1.0, 19, -1, False)], 4: [(1.0, 39, -10, False)], 5: [(1.0, 39, -10, False)]}, 40: {0: [(1.0, 140, -1, False)], 1: [(1.0, 40, -1, False)], 2: [(1.0, 60, -1, False)], 3: [(1.0, 40, -1, False)], 4: [(1.0, 40, -10, False)], 5: [(1.0, 40, -10, False)]}, 41: {0: [(1.0, 141, -1, 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-1, False)]}, 478: {0: [(1.0, 478, -1, False)], 1: [(1.0, 378, -1, False)], 2: [(1.0, 498, -1, False)], 3: [(1.0, 478, -1, False)], 4: [(1.0, 478, -10, False)], 5: [(1.0, 474, -1, False)]}, 479: {0: [(1.0, 479, -1, False)], 1: [(1.0, 379, -1, False)], 2: [(1.0, 499, -1, False)], 3: [(1.0, 479, -1, False)], 4: [(1.0, 479, -10, False)], 5: [(1.0, 475, 20, True)]}, 480: {0: [(1.0, 480, -1, False)], 1: [(1.0, 380, -1, False)], 2: [(1.0, 480, -1, False)], 3: [(1.0, 460, -1, False)], 4: [(1.0, 480, -10, False)], 5: [(1.0, 480, -10, False)]}, 481: {0: [(1.0, 481, -1, False)], 1: [(1.0, 381, -1, False)], 2: [(1.0, 481, -1, False)], 3: [(1.0, 461, -1, False)], 4: [(1.0, 481, -10, False)], 5: [(1.0, 481, -10, False)]}, 482: {0: [(1.0, 482, -1, False)], 1: [(1.0, 382, -1, False)], 2: [(1.0, 482, -1, False)], 3: [(1.0, 462, -1, False)], 4: [(1.0, 482, -10, False)], 5: [(1.0, 482, -10, False)]}, 483: {0: [(1.0, 483, -1, False)], 1: [(1.0, 383, -1, False)], 2: [(1.0, 483, -1, False)], 3: [(1.0, 463, -1, False)], 4: [(1.0, 483, -10, False)], 5: [(1.0, 483, -10, False)]}, 484: {0: [(1.0, 484, -1, False)], 1: [(1.0, 384, -1, False)], 2: [(1.0, 484, -1, False)], 3: [(1.0, 464, -1, False)], 4: [(1.0, 484, -10, False)], 5: [(1.0, 484, -10, False)]}, 485: {0: [(1.0, 485, -1, False)], 1: [(1.0, 385, -1, False)], 2: [(1.0, 485, -1, False)], 3: [(1.0, 465, -1, False)], 4: [(1.0, 485, -10, False)], 5: [(1.0, 485, -10, False)]}, 486: {0: [(1.0, 486, -1, False)], 1: [(1.0, 386, -1, False)], 2: [(1.0, 486, -1, False)], 3: [(1.0, 466, -1, False)], 4: [(1.0, 486, -10, False)], 5: [(1.0, 486, -10, False)]}, 487: {0: [(1.0, 487, -1, False)], 1: [(1.0, 387, -1, False)], 2: [(1.0, 487, -1, False)], 3: [(1.0, 467, -1, False)], 4: [(1.0, 487, -10, False)], 5: [(1.0, 487, -10, False)]}, 488: {0: [(1.0, 488, -1, False)], 1: [(1.0, 388, -1, False)], 2: [(1.0, 488, -1, False)], 3: [(1.0, 468, -1, False)], 4: [(1.0, 488, -10, False)], 5: [(1.0, 488, -10, False)]}, 489: {0: [(1.0, 489, -1, False)], 1: [(1.0, 389, -1, False)], 2: [(1.0, 489, -1, False)], 3: [(1.0, 469, -1, False)], 4: [(1.0, 489, -10, False)], 5: [(1.0, 489, -10, False)]}, 490: {0: [(1.0, 490, -1, False)], 1: [(1.0, 390, -1, False)], 2: [(1.0, 490, -1, False)], 3: [(1.0, 470, -1, False)], 4: [(1.0, 490, -10, False)], 5: [(1.0, 490, -10, False)]}, 491: {0: [(1.0, 491, -1, False)], 1: [(1.0, 391, -1, False)], 2: [(1.0, 491, -1, False)], 3: [(1.0, 471, -1, False)], 4: [(1.0, 491, -10, False)], 5: [(1.0, 491, -10, False)]}, 492: {0: [(1.0, 492, -1, False)], 1: [(1.0, 392, -1, False)], 2: [(1.0, 492, -1, False)], 3: [(1.0, 472, -1, False)], 4: [(1.0, 492, -10, False)], 5: [(1.0, 492, -10, False)]}, 493: {0: [(1.0, 493, -1, False)], 1: [(1.0, 393, -1, False)], 2: [(1.0, 493, -1, False)], 3: [(1.0, 473, -1, False)], 4: [(1.0, 493, -10, False)], 5: [(1.0, 493, -10, False)]}, 494: {0: [(1.0, 494, -1, False)], 1: [(1.0, 394, -1, False)], 2: [(1.0, 494, -1, False)], 3: [(1.0, 474, -1, False)], 4: [(1.0, 494, -10, False)], 5: [(1.0, 494, -10, False)]}, 495: {0: [(1.0, 495, -1, False)], 1: [(1.0, 395, -1, False)], 2: [(1.0, 495, -1, False)], 3: [(1.0, 475, -1, False)], 4: [(1.0, 495, -10, False)], 5: [(1.0, 495, -10, False)]}, 496: {0: [(1.0, 496, -1, False)], 1: [(1.0, 396, -1, False)], 2: [(1.0, 496, -1, False)], 3: [(1.0, 476, -1, False)], 4: [(1.0, 496, -10, False)], 5: [(1.0, 496, -10, False)]}, 497: {0: [(1.0, 497, -1, False)], 1: [(1.0, 397, -1, False)], 2: [(1.0, 497, -1, False)], 3: [(1.0, 477, -1, False)], 4: [(1.0, 497, -10, False)], 5: [(1.0, 497, -10, False)]}, 498: {0: [(1.0, 498, -1, False)], 1: [(1.0, 398, -1, False)], 2: [(1.0, 498, -1, False)], 3: [(1.0, 478, -1, False)], 4: [(1.0, 498, -10, False)], 5: [(1.0, 498, -10, False)]}, 499: {0: [(1.0, 499, -1, False)], 1: [(1.0, 399, -1, False)], 2: [(1.0, 499, -1, False)], 3: [(1.0, 479, -1, False)], 4: [(1.0, 499, -10, False)], 5: [(1.0, 499, -10, False)]}}
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00:00:00 [INFO] lastaction: None
00:00:00 [INFO] nS: 500
00:00:00 [INFO] nA: 6
00:00:00 [INFO] action_space: Discrete(6)
00:00:00 [INFO] observation_space: Discrete(500)
00:00:00 [INFO] np_random: RandomState(MT19937)
00:00:00 [INFO] s: 344
00:00:00 [INFO] spec: EnvSpec(Taxi-v3)
In [3]:
class ClosedFormAgent:
    def __init__(self, env):
        state_n, action_n = env.observation_space.n, env.action_space.n
        v = np.zeros((env.spec.max_episode_steps+1, state_n))
        q = np.zeros((env.spec.max_episode_steps+1, state_n, action_n))
        pi = np.zeros((env.spec.max_episode_steps+1, state_n))
        for t in range(env.spec.max_episode_steps-1, -1, -1):
            for s in range(state_n):
                for a in range(action_n):
                    for p, next_s, r, d in env.P[s][a]:
                        q[t, s, a] += p * (r + (1. - float(d)) * v[t+1, next_s])
                v[t, s] = q[t, s].max()
                pi[t, s] = q[t, s].argmax()
        self.pi = pi

    def reset(self, mode=None):
        self.t = 0

    def step(self, observation, reward, terminated):
        action = self.pi[self.t, observation]
        self.t += 1
        return action

    def close(self):
        pass


agent = ClosedFormAgent(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('==== 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))

00:00:01 [INFO] ==== test ====
00:00:01 [INFO] test episode 0: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 1: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 2: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 3: reward = 11.00, steps = 10
00:00:01 [INFO] test episode 4: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 5: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 6: reward = 11.00, steps = 10
00:00:01 [INFO] test episode 7: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 8: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 9: reward = 13.00, steps = 8
00:00:01 [INFO] test episode 10: reward = 11.00, steps = 10
00:00:01 [INFO] test episode 11: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 12: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 13: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 14: reward = 13.00, steps = 8
00:00:01 [INFO] test episode 15: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 16: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 17: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 18: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 19: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 20: reward = 11.00, steps = 10
00:00:01 [INFO] test episode 21: reward = 12.00, steps = 9
00:00:01 [INFO] test episode 22: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 23: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 24: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 25: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 26: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 27: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 28: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 29: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 30: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 31: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 32: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 33: reward = 12.00, steps = 9
00:00:01 [INFO] test episode 34: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 35: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 36: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 37: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 38: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 39: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 40: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 41: reward = 11.00, steps = 10
00:00:01 [INFO] test episode 42: reward = 13.00, steps = 8
00:00:01 [INFO] test episode 43: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 44: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 45: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 46: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 47: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 48: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 49: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 50: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 51: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 52: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 53: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 54: reward = 12.00, steps = 9
00:00:01 [INFO] test episode 55: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 56: reward = 11.00, steps = 10
00:00:01 [INFO] test episode 57: reward = 3.00, steps = 18
00:00:01 [INFO] test episode 58: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 59: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 60: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 61: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 62: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 63: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 64: reward = 13.00, steps = 8
00:00:01 [INFO] test episode 65: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 66: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 67: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 68: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 69: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 70: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 71: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 72: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 73: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 74: reward = 11.00, steps = 10
00:00:01 [INFO] test episode 75: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 76: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 77: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 78: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 79: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 80: reward = 12.00, steps = 9
00:00:01 [INFO] test episode 81: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 82: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 83: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 84: reward = 12.00, steps = 9
00:00:01 [INFO] test episode 85: reward = 14.00, steps = 7
00:00:01 [INFO] test episode 86: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 87: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 88: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 89: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 90: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 91: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 92: reward = 9.00, steps = 12
00:00:01 [INFO] test episode 93: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 94: reward = 6.00, steps = 15
00:00:01 [INFO] test episode 95: reward = 7.00, steps = 14
00:00:01 [INFO] test episode 96: reward = 10.00, steps = 11
00:00:01 [INFO] test episode 97: reward = 8.00, steps = 13
00:00:01 [INFO] test episode 98: reward = 4.00, steps = 17
00:00:01 [INFO] test episode 99: reward = 8.00, steps = 13
00:00:01 [INFO] average episode reward = 8.04 ± 2.53
In [5]:
env.close()