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.
CARL: context adaptive RL¶
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¶
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.
flappy-bird-env¶
Flappy Bird as a Farama Gymnasium environment.
flappy-bird-gymnasium: A Flappy Bird environment for Gymnasium¶
A simple environment for single-agent reinforcement learning algorithms on a clone of Flappy Bird, the hugely popular arcade-style mobile game. Both state and pixel observation environments are available.
gym-cellular-automata: Cellular Automata environments¶
Environments where the agent interacts with Cellular Automata by changing its cell states.
gym-jiminy: Training Robots in Jiminy¶
gym-jiminy presents an extension of the initial Gym for robotics using Jiminy, an extremely fast and light-weight simulator for poly-articulated systems using Pinocchio for physics evaluation and Meshcat for web-based 3D rendering.
gym-pybullet-drones: Environments for quadcopter control¶
A simple environment using PyBullet to simulate the dynamics of a Bitcraze Crazyflie 2.x nanoquadrotor.
gym-saturation: Environments used to prove theorems¶
An environment for guiding automated theorem provers based on saturation algorithms (e.g. Vampire).
gym-trading-env: Trading Environment¶
Gym Trading Env simulates stock (or crypto) market from historical data. It was designed to be fast and easily customizable.
matrix-mdp: Easily create discrete MDPs¶
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.
mobile-env: Environments for coordination of wireless mobile networks¶
An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
panda-gym: Robotics environments using the PyBullet physics engine¶
PyBullet based simulations of a robotic arm moving objects.
pystk2-gymnasium: SuperTuxKart races gymnasium wrapper¶
Uses a python wrapper around SuperTuxKart that allows to access the world state and to control a race.
QWOP: An environment for Bennet Foddy’s game QWOP¶
QWOP is a game about running extremely fast down a 100 meter track. With this Gymnasium environment you can train your own agents and try to beat the current world record (5.0 in-game seconds for humans and 4.7 for AI).
Safety-Gymnasium: Ensuring safety in real-world RL scenarios¶
Highly scalable and customizable Safe Reinforcement Learning library.
SimpleGrid: a simple grid environment for Gymnasium¶
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.
spark-sched-sim: Environments for scheduling DAG jobs in Apache Spark¶
spark-sched-sim simulates Spark clusters for RL-based job scheduling algorithms. Spark jobs are encoded as directed acyclic graphs (DAGs), providing opportunities to experiment with graph neural networks (GNN’s) in the RL context.
sumo-rl: Reinforcement Learning using SUMO traffic simulator¶
Gymnasium wrapper for various environments in the SUMO traffic simulator. Supports both single and multiagent settings (using pettingzoo).
tmrl: TrackMania 2020 through RL¶
tmrl is a distributed framework for training Deep Reinforcement Learning AIs in real-time applications. It is demonstrated on the TrackMania 2020 video game.
EV2Gym: A Realistic EV-V2G-Gym Simulator for EV Smart Charging¶
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.
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¶
gym-derk: GPU accelerated MOBA environment¶
A 3v3 MOBA environment where you train creatures to fight each other.
SlimeVolleyGym: A simple environment for Slime Volleyball game¶
A simple environment for benchmarking single and multi-agent reinforcement learning algorithms on a clone of Slime Volleyball game.
Unity ML Agents: Environments for Unity game engine¶
Gym (and PettingZoo) wrappers for arbitrary and premade environments with the Unity game engine.
PGE: Parallel Game Engine¶
Uses The Open 3D Engine for AI simulations and can interoperate with the Gym. Uses PyBullet physics.
Robotics environments¶
PyFlyt: UAV Flight Simulator Environments for Reinforcement Learning Research¶
A library for testing reinforcement learning algorithms on various UAVs. It is built on the Bullet physics engine, offers flexible rendering options, time-discrete steppable physics, Python bindings, and support for custom drones of any configuration, be it biplanes, quadcopters, rockets, and anything you can think of.
MarsExplorer: Environments for controlling robot on Mars¶
Mars Explorer is a Gym compatible environment designed and developed as an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of an unknown terrain.
robo-gym: Real-world and simulation robotics¶
Robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real-world robotics.
Offworld-gym: Control real robots remotely for free¶
Gym environments that let you control real robots in a laboratory via the internet.
safe-control-gym: Evaluate safety of RL algorithms¶
Evaluate safety, robustness and generalization via PyBullet based CartPole and Quadrotor environments—with CasADi (symbolic) a priori dynamics and constraints.
gym-softrobot: Soft-robotics environments¶
A large-scale benchmark for co-optimizing the design and control of soft robots.
iGibson: Photorealistic and interactive robotics environments¶
A simulation environment with high-quality realistic scenes, with interactive physics using PyBullet.
DexterousHands: Dual dexterous hand manipulation tasks¶
This is a library that provides dual dexterous hand manipulation tasks through Isaac Gym.
OmniIsaacGymEnvs: Gym environments for NVIDIA Omniverse Isaac ¶
Reinforcement Learning Environments for Omniverse Isaac simulator.
Autonomous Driving environments¶
gym-duckietown: Lane-following for self-driving cars¶
A lane-following simulator built for the Duckietown project (small-scale self-driving car course).
gym-electric-motor: Gym environments for electric motor simulations¶
An environment for simulating a wide variety of electric drives taking into account different types of electric motors and converters.
CommonRoad-RL: Motion planning for traffic scenarios ¶
A Gym for solving motion planning problems for various traffic scenarios compatible with CommonRoad benchmarks, which provides configurable rewards, action spaces, and observation spaces.
racing_dreamer: Latent imagination in autonomous racing¶
Train a model-based RL agent in simulation and, without finetuning, transfer it to small-scale race cars.
l2r: Multimodal control environment where agents learn how to race¶
An open-source reinforcement learning environment for autonomous racing.
racecar_gym: Miniature racecar env using PyBullet¶
A gym environment for a miniature racecar using the PyBullet physics engine.
Other environments¶
Connect-4-gym : An environment for practicing self playing¶
Connect-4-Gym is an environment designed for creating AIs that learn by playing against themselves and assigning them an Elo rating. This environment can be used to train and evaluate reinforcement learning agents on the classic board game Connect Four.
CompilerGym: Optimise compiler tasks¶
Reinforcement learning environments for compiler optimization tasks, such as LLVM phase ordering, GCC flag tuning, and CUDA loop nest code generation.
gym-sokoban: 2D Transportation Puzzles¶
The environment consists of transportation puzzles in which the player’s goal is to push all boxes to the warehouse’s storage locations.
NLPGym: A toolkit to develop RL agents to solve NLP tasks¶
NLPGym provides interactive environments for standard NLP tasks such as sequence tagging, question answering, and sequence classification.
ShinRL: Environments for evaluating RL algorithms¶
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives (Deep RL Workshop 2021)
gymnax: Hardware Accelerated RL Environments¶
RL Environments in JAX which allows for highly vectorised environments with support for a number of environments, Gym, MinAtari, bsuite and more.
gym-anytrading: Financial trading environments for FOREX and STOCKS¶
AnyTrading is a collection of Gym environments for reinforcement learning-based trading algorithms with a great focus on simplicity, flexibility, and comprehensiveness.
gym-mtsim: Financial trading for MetaTrader 5 platform¶
MtSim is a simulator for the MetaTrader 5 trading platform for reinforcement learning-based trading algorithms.
openmodelica-microgrid-gym: Environments for controlling power electronic converters in microgrids¶
The OpenModelica Microgrid Gym (OMG) package is a software toolbox for the simulation and control optimization of microgrids based on energy conversion by power electronic converters.
GymFC: A flight control tuning and training framework¶
GymFC is a modular framework for synthesizing neuro-flight controllers. Has been used to generate policies for the world’s first open-source neural network flight control firmware Neuroflight.