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.
A benchmark library for [Dynamic Algorithm Configuration](https://www.automl.org/dynamic-algorithm-configuration/). Its focus is on reproducibility and comparability of different DAC methods as well as easy analysis of the optimization process.
A simple environment for single-agent reinforcement learning algorithms on a clone of [Flappy Bird](https://en.wikipedia.org/wiki/Flappy_Bird), the hugely popular arcade-style mobile game. Both state and pixel observation environments are available.
gym-jiminy presents an extension of the initial Gym for robotics using [Jiminy](https://github.com/duburcqa/jiminy), an extremely fast and light-weight simulator for poly-articulated systems using Pinocchio for physics evaluation and Meshcat for web-based 3D rendering.
A simple environment using [PyBullet](https://github.com/bulletphysics/bullet3) to simulate the dynamics of a [Bitcraze Crazyflie 2.x](https://www.bitcraze.io/documentation/hardware/crazyflie_2_1/crazyflie_2_1-datasheet.pdf) nanoquadrotor.
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.
Uses a [python wrapper](https://github.com/bpiwowar/pystk2) around [SuperTuxKart](https://supertuxkart.net/fr/Main_Page) that allows to access the world state and to control a race.
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).
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](https://github.com/ArchieGertsman/spark-sched-sim)
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.
Gymnasium wrapper for various environments in the SUMO traffic simulator. Supports both single and multiagent settings (using [pettingzoo](https://pettingzoo.farama.org/)).
tmrl is a distributed framework for training Deep Reinforcement Learning AIs in real-time applications. It is demonstrated on the TrackMania 2020 video game.
There are a large number of third-party environments using various versions of [Gym](https://github.com/openai/gym).
Many of these can be adapted to work with gymnasium (see [Compatibility with Gym](https://gymnasium.farama.org/content/gym_compatibility/)), but are not guaranteed to be fully functional.
Uses The [Open 3D Engine](https://www.o3de.org/) for AI simulations and can interoperate with the Gym. Uses [PyBullet](https://github.com/bulletphysics/bullet3) physics.
### [PyFlyt: UAV Flight Simulator Environments for Reinforcement Learning Research](https://jjshoots.github.io/PyFlyt/index.html#)
A library for testing reinforcement learning algorithms on various UAVs.
It is built on the [Bullet](https://github.com/bulletphysics/bullet3) 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.
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.
Evaluate safety, robustness and generalization via PyBullet based CartPole and Quadrotor environments—with [CasADi](https://web.casadi.org) (symbolic) *a priori* dynamics and constraints.
A Gym for solving motion planning problems for various traffic scenarios compatible with [CommonRoad benchmarks](https://commonroad.in.tum.de/scenarios), which provides configurable rewards, action spaces, and observation spaces.
### [Connect-4-gym : An environment for practicing self playing](https://github.com/lucasBertola/Connect-4-Gym-env-Reinforcement-learning)
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.
[NLPGym](https://arxiv.org/pdf/2011.08272v1.pdf) provides interactive environments for standard NLP tasks such as sequence tagging, question answering, and sequence classification.
AnyTrading is a collection of Gym environments for reinforcement learning-based trading algorithms with a great focus on simplicity, flexibility, and comprehensiveness.
### [openmodelica-microgrid-gym: Environments for controlling power electronic converters in microgrids](https://github.com/upb-lea/openmodelica-microgrid-gym)
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 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](https://github.com/wil3/neuroflight).
