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("Taxi-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)
We provide the environment; you provide the algorithm.
You can write your agent using your existing numerical computation library, such as TensorFlow or Theano.