Gym安装学习

Gym是一个用于开发比较强化学习算法的工具包。其最大的特点是可以可视化数据。

安装

使用镜像安装：

pip install gym-i http://pypi.douban.com/simple --trusted-host pypi.douban.com

环境

运行下述代码，即可测试环境：

import gym
env = gym.make('CartPole-v1')
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))
            break
env.close()

我运行后报错，缺少了pygame，用上述镜像代码安装即可。
这是一个经典的智能体和环境的循环，每一个时间步，智能体都会选择一个action，环境返回一个observation 和 reward。
在过程的开始调用reset（）函数，它会返回一个初始值observation。
环境的step函数会返回四个值：

• observation(object)，对环境的中要观察的特定的对象
• reward(float)，前一个动作获得的奖励量
• done(boolean)，是否需要reset环境，done表示True已经终止。
• info(dict)，对调试有用的信息，相当于作弊信息，一般不用。

Space 通常包括一个action_space和一个observation_space

import gym
env = gym.make('CartPole-v0')
print(env.action_space)
#> Discrete(2)
print(env.observation_space)
#> Box(4,)

print(env.observation_space.high)
#> array([ 2.4       ,         inf,  0.20943951,         inf])
print(env.observation_space.low)
#> array([-2.4       ,        -inf, -0.20943951,        -inf])

可以选择的环境：

• Classic control and toy text: complete small-scale tasks, mostly from the RL literature. They’re here to get you started.
• Algorithmic: perform computations such as adding multi-digit numbers and reversing sequences. One might object that these tasks are easy for a computer. The challenge is to learn these algorithms purely from examples. These tasks have the nice property that it’s easy to vary the difficulty by varying the sequence length.
• Atari: play classic Atari games. We’ve integrated the Arcade Learning Environment (which has had a big impact on reinforcement learning research) in an easy-to-install form.
• 2D and 3D robots: control a robot in simulation. These tasks use the MuJoCo physics engine, which was designed for fast and accurate robot simulation.