Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.
官网链接：http://gym.openai.com/
Github: https://github.com/openai/gym
import gym
env = gym.make("CartPole-v1")
observation = env.reset()
for _ in range(1000):
env.render()
action = env.action_space.sample() # your agent here (this takes random actions)
observation, reward, done, info = env.step(action)
if done:
observation = env.reset()
env.close()
