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", render_mode="human")
observation, info = env.reset(seed=42)
for _ in range(1000):
   action = policy(observation)  # User-defined policy function
   observation, reward, terminated, truncated, info = env.step(action)
   if terminated or truncated:
      observation, info = env.reset()
env.close()

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

Gym Documentation