External Environments

First-Party Environments

The Farama Foundation maintains a number of other projects, which use the Gymnasium API, environments include: gridworlds (Minigrid), robotics (Gymnasium-Robotics), 3D navigation (Miniworld), web interaction (MiniWoB++), arcade games (Arcade Learning Environment), Doom (ViZDoom), Meta-objective robotics (Metaworld), autonomous driving (HighwayEnv), Retro Games (stable-retro), and many more.

The Farama Foundation also maintains alternate APIs for RL, including: multi-agent RL (PettingZoo), offline-RL (Minari), multi-objective RL (MO-Gymnasium), goal-RL (Gymnasium-Robotics).

Third-party environments with Gymnasium

This page contains environments which are not maintained by Farama Foundation and, as such, cannot be guaranteed to function as intended.

If you’d like to contribute an environment, please reach out on Discord, then submit a PR by editing this file.

Autonomous Driving environments

Autonomous Vehicle and traffic management.

Biological / Medical environments

Interacting with Biological Systems.

Economic / Financial environments

Everything Economics related.

Electrical / Energy environments

Manage the flow of Electrons.

  • EV2Gym: A Realistic EV-V2G-Gym Simulator for EV Smart Charging

    Gymnasium version dependency GitHub stars

    EV2Gym is a fully customizable and easily configurable environment for Electric Vehicle (EV) smart charging simulations on a small and large scale. Also, includes non-RL baseline implementations such as mathematical programming, model predictive control, and heuristics.

Game environments

Board Games, Video Games and all other interactive entrainment mediums.

Mathematics / Computational

Reduce computational compute, prove math theorems, and more.

Robotics environments

Autonomous Robots.

Telecommunication Systems environments

Interact and/or manage wireless and/or wired telecommunication systems.

Other

  • Buffalo-Gym: Multi-Armed Bandit Gymnasium

    Gymnasium version dependency GitHub stars

    Buffalo-Gym is a Multi-Armed Bandit (MAB) gymnasium built primarily to assist in debugging RL implementations. MABs are often easy to reason about what the agent is learning and whether it is correct. Buffalo-gym encompasses Bandits, Contextual bandits, and contextual bandits with aliasing.

  • CARL: context adaptive RL

    Gymnasium version dependency GitHub stars

    Contextual extensions of popular reinforcement learning environments that enable training and test distributions for generalization, e.g. CartPole with variable pole lengths or Brax robots with different ground frictions.

  • DACBench: a benchmark for Dynamic Algorithm Configuration

    Gymnasium version dependency GitHub stars

    A benchmark library for Dynamic Algorithm Configuration. Its focus is on reproducibility and comparability of different DAC methods as well as easy analysis of the optimization process.

  • gym-cellular-automata: Cellular Automata environments

    Gymnasium version dependency GitHub stars

    Environments where the agent interacts with Cellular Automata by changing its cell states.

  • Gym-Gridworlds: Collection of Customizable Minimalistic Gridworlds

    Gymnasium version dependency GitHub stars

    The default class implements a “go-to goal”, but it can be easily customized for different tasks, with a variety of grids, rewards, dynamics, and tasks. It supports different observation types (discrete, coordinates, binary, pixels, partial). Useful for quickly testing and prototyping RL algorithms, both tabular and with function approximation.

  • matrix-mdp: Easily create discrete MDPs

    Gymnasium version dependency GitHub stars

    An environment to easily implement discrete MDPs as gym environments. Turn a set of matrices (P_0(s), P(s'| s, a) and R(s', s, a)) into a gym environment that represents the discrete MDP ruled by these dynamics.

  • SimpleGrid: a simple grid environment for Gymnasium

    Gymnasium version dependency GitHub stars

    SimpleGrid is a super simple and minimal grid environment for Gymnasium. It is easy to use and customise and it is intended to offer an environment for rapidly testing and prototyping different RL algorithms.

Third-Party Environments using Gym

There are a large number of third-party environments using various versions of Gym. Many of these can be adapted to work with gymnasium (see Compatibility with Gym), but are not guaranteed to be fully functional.

Video Game environments

Robotics environments

Autonomous Driving environments

Other environments