案例【不倒翁】

import gym
import time

'''
    基于强化学习实现不倒翁特性：自动平衡恢复
'''
if __name__ == "__main__":
    env = gym.make('CartPole-v0')
    for i_episode in range(20):
        observation = env.reset()
        for t in range(100):
            env.render()
            # print(observation)
            action = env.action_space.sample()
            observation, reward, done, info = env.step(action)
            if done:
                # print("Episode finished after {} timesteps".format(t + 1))
                print(observation)
                time.sleep(1)  # 暂停以下，便于观察
                break

    time.sleep(10)  # 停顿10s
    env.close()  # 关闭图形化界面

案例【小车自适应翻越小沟】

# -*- coding: utf-8 -*-
import gym
import time

'''
    基于强化学习实现小车自适应翻越小沟
'''
class BespokeAgent:
    def __init__(self, env):
        pass

    def decide(self, observation):
        position, velocity = observation
        lb = min(-0.09 * (position + 0.25) ** 2 + 0.03, 0.3 * (position + 0.9) ** 4 - 0.008)
        ub = -0.07 * (position + 0.38) ** 2 + 0.06
        if lb < velocity < ub:
            action = 2
        else:
            action = 0
        return action  # 返回动作

    def learn(self, *args):  # 学习
        pass

    def play_ones(self, env, agent, render=False, train=False):
        episode_reward = 0  # 记录回合总奖励，初始值为0
        observation = env.reset()  # 重置游戏环境，开始新回合
        while True:  # 不断循环，直到回合结束
            if render:  # 判断是否显示
                env.render()  # 显示图形界面，可以用env.close()关闭
            action = agent.decide(observation)
            next_observation, reward, done, _ = env.step(action)  # 执行动作
            episode_reward += reward  # 搜集回合奖励
            if train:  # 判断是否训练智能体
                break
            observation = next_observation
        return episode_reward  # 返回回合总奖励

if __name__ == '__main__':
    env = gym.make('MountainCar-v0')
    env.seed(0)  # 设置随机数种子，只是为了让结果可以精确复现，一般情况下可以删除

    agent = BespokeAgent(env)
    for _ in range(100):
        episode_reward = agent.play_ones(env, agent, render=True)
        print('回合奖励={}'.format(episode_reward))

    time.sleep(10)  # 停顿10s
    env.close()  # 关闭图形化界面

Ref:

1. 强化学习代码实战