# Gym

## Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pongor Pinball.

### Open source interface to reinforcement learning tasks.

The gym library provides an easy-to-use suite of reinforcement learning tasks.

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()

### We provide the environment; you provide the algorithm.

You can write your agent using your existing numerical computation library, such as TensorFlow or Theano.