Gym

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.
RandomAgent on CartPole-v1
RandomAgent on Pendulum-v0
RandomAgent on SpaceInvaders-v0
RandomAgent on LunarLander-v2
RandomAgent on Ant-v2

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.

Start learning

Read the docs, download the toolkit and start training your agents.

View documentation View on GitHub