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 …
Utils - Gym Documentation
>>> import gym >>> from gym.utils.play import play >>> play(gym.make("CarRacing-v1", render_mode="rgb_array"), keys_to_action={ ... "w": np.array([0, 0.7, 0]), ... "a": np.array([-1, …
Tutorials - Gym Documentation
Good Algorithmic Introduction to Reinforcement Learning showcasing how to use Gym API for Training Agents. https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch …
Basic Usage - Gym Documentation
Gym implements the classic “agent-environment loop”: The agent performs some actions in the environment (usually by passing some control inputs to the environment, e.g. torque inputs of …
Search - Gym Documentation
If you're already using the latest release of Gym (v0.26.2), then you can switch to v0.27.0 of Gymnasium by simply replacing import gym with import gymnasium as gym with no additional …
Core - Gym Documentation
Core # gym.Env # gym.Env.step(self, action: ActType) → Tuple[ObsType, float, bool, bool, dict] # Run one timestep of the environment’s dynamics. When end of episode is reached, you are …
Wrappers - Gym Documentation
If you're already using the latest release of Gym (v0.26.2), then you can switch to v0.27.0 of Gymnasium by simply replacing import gym with import gymnasium as gym with no additional …
Third Party Environments - Gym Documentation
Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface. TORCS is the open-rource realistic car racing simulator recently used as …
MuJoCo - Gym Documentation
Instructions on installing the MuJoCo engine can be found at their website and GitHub repository. Using MuJoCo with OpenAI Gym also requires that the framework mujoco-py be installed, …
Make your own custom environment - Gym Documentation
Before learning how to create your own environment you should check out the documentation of Gym’s API. We will be concerned with a subset of gym-examples that looks like this: