安装

pip install gym

编码运行

#coding=utf8

import gym
import numpy as np

env = gym.make('CartPole-v0')

max_number_of_steps = 200   # 每一场游戏的最高得分
#---------获胜的条件是最近100场平均得分高于195-------------
goal_average_steps = 195
num_consecutive_iterations = 100
#----------------------------------------------------------
num_episodes = 5000 # 共进行5000场游戏
last_time_steps = np.zeros(num_consecutive_iterations)  # 只存储最近100场的得分（可以理解为是一个容量为100的栈）

# q_table是一个256*2的二维数组
# 离散化后的状态共有4^4=256中可能的取值，每种状态会对应一个行动
# q_table[s][a]就是当状态为s时作出行动a的有利程度评价值
# 我们的AI模型要训练学习的就是这个映射关系表
q_table = np.random.uniform(low=-1, high=1, size=(4 ** 4, env.action_space.n))

# 分箱处理函数，把[clip_min,clip_max]区间平均分为num段，位于i段区间的特征值x会被离散化为i
def bins(clip_min, clip_max, num):
    return np.linspace(clip_min, clip_max, num + 1)[1:-1]

# 离散化处理，将由4个连续特征值组成的状态矢量转换为一个0~~255的整数离散值
def digitize_state(observation):
    # 将矢量打散回4个连续特征值
    cart_pos, cart_v, pole_angle, pole_v = observation
    # 分别对各个连续特征值进行离散化（分箱处理）
    digitized = [np.digitize(cart_pos, bins=bins(-2.4, 2.4, 4)),
                 np.digitize(cart_v, bins=bins(-3.0, 3.0, 4)),
                 np.digitize(pole_angle, bins=bins(-0.5, 0.5, 4)),
                 np.digitize(pole_v, bins=bins(-2.0, 2.0, 4))]
    # 将4个离散值再组合为一个离散值，作为最终结果
    return sum([x * (4 ** i) for i, x in enumerate(digitized)])

# 根据本次的行动及其反馈（下一个时间步的状态），返回下一次的最佳行动
def get_action(state, action, observation, reward, episode):
    next_state = digitize_state(observation)
    epsilon = 0.5 * (0.99 ** episode)
    if  epsilon <= np.random.uniform(0, 1):
        next_action = np.argmax(q_table[next_state])
    else:
        next_action = np.random.choice([0, 1])
    #-------------------------------------训练学习，更新q_table----------------------------------
    alpha = 0.2     # 学习系数α
    gamma = 0.99    # 报酬衰减系数γ
    q_table[state, action] = (1 - alpha) * q_table[state, action] + alpha * (reward + gamma * q_table[next_state, next_action])
    # -------------------------------------------------------------------------------------------
    return next_action, next_state

# 重复进行一场场的游戏
for episode in range(num_episodes):
    observation = env.reset()   # 初始化本场游戏的环境
    state = digitize_state(observation)     # 获取初始状态值
    action = np.argmax(q_table[state])      # 根据状态值作出行动决策
    episode_reward = 0
    # 一场游戏分为一个个时间步
    for t in range(max_number_of_steps):
        env.render()    # 更新并渲染游戏画面
        observation, reward, done, info = env.step(action)
        # 对致命错误行动进行极大力度的惩罚，让模型恨恨地吸取教训
        if done:
            reward = -200
        action, state = get_action(state, action, observation, reward, episode)
        if done:
            print('%d Episode finished after %f time steps / mean %f' % (episode, t + 1,
                last_time_steps.mean()))
            last_time_steps = np.hstack((last_time_steps[1:], [t + 1]))
            break
            # 如果最近100场平均得分高于195
        if (last_time_steps.mean() >= goal_average_steps):
            print('Episode %d train agent successfuly!' % episode)
            break

    print('Failed!')

参考文章