Gym documentation

Gym is a standard API for reinforcement learning, and a diverse collection of reference environments.

The Gym interface is simple, pythonic, and capable of representing general RL problems:

import gym env = gym.make("LunarLander-v2") observation, info = env.reset(seed=42, return_info=True) for _ in range(1000): env.render() action = policy(observation) # User-defined policy function observation, reward, done, info = env.step(action) if done: observation, info = env.reset(return_info=True) env.close()