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()