OpenAI gym 环境库

import gym
from RL_brain import DeepQNetwork

env = gym.make('CartPole-v0')   # 定义使用 gym 库中的那一个环境
env = env.unwrapped # 不做这个会有很多限制

print(env.action_space) # 查看这个环境中可用的 action 有多少个
print(env.observation_space)    # 查看这个环境中可用的 state 的 observation 有多少个
print(env.observation_space.high)   # 查看 observation 最高取值
print(env.observation_space.low)    # 查看 observation 最低取值

# 定义使用 DQN 的算法
RL = DeepQNetwork(n_actions=env.action_space.n,
                  n_features=env.observation_space.shape[0],
                  learning_rate=0.01, e_greedy=0.9,
                  replace_target_iter=100, memory_size=2000,
                  e_greedy_increment=0.0008,)

total_steps = 0 # 记录步数

for i_episode in range(100):

    # 获取回合 i_episode 第一个 observation
    observation = env.reset()
    ep_r = 0
    while True:
        env.render()    # 刷新环境

        action = RL.choose_action(observation)  # 选行为

        observation_, reward, done, info = env.step(action) # 获取下一个 state

        x, x_dot, theta, theta_dot = observation_   # 细分开, 为了修改原配的 reward

        # x 是车的水平位移, 所以 r1 是车越偏离中心, 分越少
        # theta 是棒子离垂直的角度, 角度越大, 越不垂直. 所以 r2 是棒越垂直, 分越高

        x, x_dot, theta, theta_dot = observation_
        r1 = (env.x_threshold - abs(x))/env.x_threshold - 0.8
        r2 = (env.theta_threshold_radians - abs(theta))/env.theta_threshold_radians - 0.5
        reward = r1 + r2   # 总 reward 是 r1 和 r2 的结合, 既考虑位置, 也考虑角度, 这样 DQN 学习更有效率

        # 保存这一组记忆
        RL.store_transition(observation, action, reward, observation_)

        if total_steps > 1000:
            RL.learn()  # 学习

        ep_r += reward
        if done:
            print('episode: ', i_episode,
                  'ep_r: ', round(ep_r, 2),
                  ' epsilon: ', round(RL.epsilon, 2))
            break

        observation = observation_
        total_steps += 1
# 最后输出 cost 曲线
RL.plot_cost()

import gym
from RL_brain import DeepQNetwork

env = gym.make('MountainCar-v0')
env = env.unwrapped

print(env.action_space)
print(env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)

RL = DeepQNetwork(n_actions=3, n_features=2, learning_rate=0.001, e_greedy=0.9,
                  replace_target_iter=300, memory_size=3000,
                  e_greedy_increment=0.0001,)

total_steps = 0


for i_episode in range(10):

    observation = env.reset()
    ep_r = 0
    while True:
        env.render()

        action = RL.choose_action(observation)

        observation_, reward, done, info = env.step(action)

        position, velocity = observation_

        # 车开得越高 reward 越大
        reward = abs(position - (-0.5))

        RL.store_transition(observation, action, reward, observation_)

        if total_steps > 1000:
            RL.learn()

        ep_r += reward
        if done:
            get = '| Get' if observation_[0] >= env.unwrapped.goal_position else '| ----'
            print('Epi: ', i_episode,
                  get,
                  '| Ep_r: ', round(ep_r, 4),
                  '| Epsilon: ', round(RL.epsilon, 2))
            break

        observation = observation_
        total_steps += 1

RL.plot_cost()