Gymnasium Documentation (original) (raw)
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An API standard for reinforcement learning with a diverse collection of reference environments
Gymnasium is a maintained fork of OpenAI’s Gym library. The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a migration guide for old Gym environments:
import gymnasium as gym
Initialise the environment
env = gym.make("LunarLander-v3", render_mode="human")
Reset the environment to generate the first observation
observation, info = env.reset(seed=42) for _ in range(1000): # this is where you would insert your policy action = env.action_space.sample()
# step (transition) through the environment with the action
# receiving the next observation, reward and if the episode has terminated or truncated
observation, reward, terminated, truncated, info = env.step(action)
# If the episode has ended then we can reset to start a new episode
if terminated or truncated:
observation, info = env.reset()env.close()