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, terminated, truncated, info = env.step(action)

      if terminated or truncated:
         observation, info = env.reset(return_info=True)
   env.close()