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<p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
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<article role="main">
<section id="third-party-environments">
<h1>Third-Party Environments<a class="headerlink" href="#third-party-environments" title="Permalink to this heading">#</a></h1>
<p>The Farama Foundation maintains a number of other <a class="reference external" href="https://farama.org/projects">projects</a>, most of which use Gymnasium. Topics include:
multi-agent RL (<a class="reference external" href="https://pettingzoo.farama.org/">PettingZoo</a>),
offline-RL (<a class="reference external" href="https://minari.farama.org/">Minari</a>),
gridworlds (<a class="reference external" href="https://minigrid.farama.org/">Minigrid</a>),
robotics (<a class="reference external" href="https://robotics.farama.org/">Gymnasium-Robotics</a>),
multi-objective RL (<a class="reference external" href="https://mo-gymnasium.farama.org/">MO-Gymnasium</a>)
many-agent RL (<a class="reference external" href="https://magent2.farama.org/">MAgent2</a>),
3D navigation (<a class="reference external" href="https://miniworld.farama.org/">Miniworld</a>), and many more.</p>
<p><em>This page contains environments which are not maintained by Farama Foundation and, as such, cannot be guaranteed to function as intended.</em></p>
<p><em>If you’d like to contribute an environment, please reach out on <a class="reference external" href="https://discord.gg/nHg2JRN489">Discord</a>.</em></p>
<section id="highway-env-autonomous-driving-and-tactical-decision-making-tasks">
<h2><a class="reference external" href="https://github.com/eleurent/highway-env">highway-env: Autonomous driving and tactical decision-making tasks</a><a class="headerlink" href="#highway-env-autonomous-driving-and-tactical-decision-making-tasks" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/eleurent/highway-env" /></span></a></p>
<p>An environment for behavioral planning in autonomous driving, with an emphasis on high-level perception and decision rather than low-level sensing and control.</p>
</section>
<section id="sumo-rl-reinforcement-learning-using-sumo-traffic-simulator">
<h2><a class="reference external" href="https://github.com/LucasAlegre/sumo-rl">sumo-rl: Reinforcement Learning using SUMO traffic simulator</a><a class="headerlink" href="#sumo-rl-reinforcement-learning-using-sumo-traffic-simulator" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.3-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/LucasAlegre/sumo-rl" /></span></a></p>
<p>Gymnasium wrapper for various environments in the SUMO traffic simulator. Supports both single and multiagent settings (using <a class="reference external" href="https://pettingzoo.farama.org/">pettingzoo</a>).</p>
</section>
<section id="panda-gym-robotics-environments-using-the-pybullet-physics-engine">
<h2><a class="reference external" href="https://github.com/qgallouedec/panda-gym/">panda-gym: Robotics environments using the PyBullet physics engine</a><a class="headerlink" href="#panda-gym-robotics-environments-using-the-pybullet-physics-engine" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.3-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/qgallouedec/panda-gym" /></span></a></p>
<p>PyBullet based simulations of a robotic arm moving objects.</p>
</section>
<section id="tmrl-trackmania-2020-through-rl">
<h2><a class="reference external" href="https://github.com/trackmania-rl/tmrl/">tmrl: TrackMania 2020 through RL</a><a class="headerlink" href="#tmrl-trackmania-2020-through-rl" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/trackmania-rl/tmrl" /></span></a></p>
<p>tmrl is a distributed framework for training Deep Reinforcement Learning AIs in real-time applications. It is demonstrated on the TrackMania 2020 video game.</p>
</section>
<section id="safety-gymnasium-ensuring-safety-in-real-world-rl-scenarios">
<h2><a class="reference external" href="https://github.com/PKU-MARL/safety-gymnasium">Safety-Gymnasium: Ensuring safety in real-world RL scenarios</a><a class="headerlink" href="#safety-gymnasium-ensuring-safety-in-real-world-rl-scenarios" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.3-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/PKU-MARL/safety-gymnasium" /></span></a></p>
<p>Highly scalable and customizable Safe Reinforcement Learning library.</p>
</section>
<section id="stable-retro-classic-retro-games-a-maintained-version-of-openai-retro">
<h2><a class="reference external" href="https://github.com/MatPoliquin/stable-retro">stable-retro: Classic retro games, a maintained version of OpenAI Retro</a><a class="headerlink" href="#stable-retro-classic-retro-games-a-maintained-version-of-openai-retro" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/MatPoliquin/stable-retro" /></span></a></p>
<p>Supported fork of gym-retro: turn classic video games into Gymnasium environments.</p>
</section>
<section id="flappy-bird-gymnasium-a-flappy-bird-environment-for-gymnasium">
<h2><a class="reference external" href="https://github.com/markub3327/flappy-bird-gymnasium">flappy-bird-gymnasium: A Flappy Bird environment for Gymnasium</a><a class="headerlink" href="#flappy-bird-gymnasium-a-flappy-bird-environment-for-gymnasium" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/markub3327/flappy-bird-gymnasium" /></span></a></p>
<p>A simple environment for single-agent reinforcement learning algorithms on a clone of <a class="reference external" href="https://en.wikipedia.org/wiki/Flappy_Bird">Flappy Bird</a>, the hugely popular arcade-style mobile game. Both state and pixel observation environments are available.</p>
</section>
<section id="matrix-mdp-easily-create-discrete-mdps">
<h2><a class="reference external" href="https://github.com/Paul-543NA/matrix-mdp-gym">matrix-mdp: Easily create discrete MDPs</a><a class="headerlink" href="#matrix-mdp-easily-create-discrete-mdps" title="Permalink to this heading">#</a></h2>
<p><a class="reference internal" href="#"><span class="xref myst"><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.2-blue" /></span></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="GitHub stars" src="https://img.shields.io/github/stars/Paul-543NA/matrix-mdp-gym" /></span></a></p>
<p>An environment to easily implement discrete MDPs as gym environments. Turn a set of matrices (<code class="docutils literal notranslate"><span class="pre">P_0(s)</span></code>, <code class="docutils literal notranslate"><span class="pre">P(s'|</span> <span class="pre">s,</span> <span class="pre">a)</span></code> and <code class="docutils literal notranslate"><span class="pre">R(s',</span> <span class="pre">s,</span> <span class="pre">a)</span></code>) into a gym environment that represents the discrete MDP ruled by these dynamics.</p>
</section>
</section>
<section id="third-party-environments-using-gym">
<h1>Third-Party Environments using Gym<a class="headerlink" href="#third-party-environments-using-gym" title="Permalink to this heading">#</a></h1>
<p>There are a large number of third-party environments using various versions of <a class="reference external" href="https://github.com/openai/gym">Gym</a>.
Many of these can be adapted to work with gymnasium (see <a class="reference external" href="https://gymnasium.farama.org/content/gym_compatibility/">Compatibility with Gym</a>), but are not guaranteed to be fully functional.</p>
<section id="video-game-environments">
<h2>Video Game environments<a class="headerlink" href="#video-game-environments" title="Permalink to this heading">#</a></h2>
<section id="gym-derk-gpu-accelerated-moba-environment">
<h3><a class="reference external" href="https://gym.derkgame.com/">gym-derk: GPU accelerated MOBA environment</a><a class="headerlink" href="#gym-derk-gpu-accelerated-moba-environment" title="Permalink to this heading">#</a></h3>
<p>This is a 3v3 MOBA environment where you train creatures to fight each other. It runs entirely on the GPU so you can easily have hundreds of instances running in parallel. There are around 15 items for the creatures, 60 “senses”, 5 actions, and roughly 23 tweakable rewards. It’s also possible to benchmark an agent against other agents online. It’s available for free for training for personal use, and otherwise costs money; see licensing details on the website</p>
</section>
<section id="slimevolleygym-a-simple-environment-for-single-and-multi-agent-reinforcement-learning">
<h3><a class="reference external" href="https://github.com/hardmaru/slimevolleygym">SlimeVolleyGym: A simple environment for single and multi-agent reinforcement learning</a><a class="headerlink" href="#slimevolleygym-a-simple-environment-for-single-and-multi-agent-reinforcement-learning" title="Permalink to this heading">#</a></h3>
<p>A simple environment for benchmarking single and multi-agent reinforcement learning algorithms on a clone of Slime Volleyball game. The only dependencies are gym and NumPy. Both state and pixel observation environments are available. The motivation of this environment is to easily enable trained agents to play against each other, and also facilitate the training of agents directly in a multi-agent setting, thus adding an extra dimension for evaluating an agent’s performance.</p>
</section>
<section id="unity-ml-agents-environments-for-unity-game-engine">
<h3><a class="reference external" href="https://github.com/Unity-Technologies/ml-agents">Unity ML Agents: Environments for Unity game engine</a><a class="headerlink" href="#unity-ml-agents-environments-for-unity-game-engine" title="Permalink to this heading">#</a></h3>
<p>Gym wrappers for arbitrary and premade environments with the Unity game engine.</p>
</section>
<section id="pge-parallel-game-engine">
<h3><a class="reference external" href="https://github.com/222464/PGE">PGE: Parallel Game Engine</a><a class="headerlink" href="#pge-parallel-game-engine" title="Permalink to this heading">#</a></h3>
<p>PGE is a FOSS 3D engine for AI simulations and can interoperate with the Gym. Contains environments with modern 3D graphics, and uses Bullet for physics.</p>
</section>
</section>
<section id="robotics-environments">
<h2>Robotics environments<a class="headerlink" href="#robotics-environments" title="Permalink to this heading">#</a></h2>
<section id="gym-jiminy-training-robots-in-jiminy">
<h3><a class="reference external" href="https://github.com/duburcqa/jiminy">gym-jiminy: Training Robots in Jiminy</a><a class="headerlink" href="#gym-jiminy-training-robots-in-jiminy" title="Permalink to this heading">#</a></h3>
<p>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.</p>
</section>
<section id="gym-pybullet-drones-environments-for-quadcopter-control">
<h3><a class="reference external" href="https://github.com/JacopoPan/gym-pybullet-drones">gym-pybullet-drones: Environments for quadcopter control</a><a class="headerlink" href="#gym-pybullet-drones-environments-for-quadcopter-control" title="Permalink to this heading">#</a></h3>
<p>A simple environment using <a class="reference external" href="https://github.com/bulletphysics/bullet3">PyBullet</a> to simulate the dynamics of a <a class="reference external" href="https://www.bitcraze.io/documentation/hardware/crazyflie_2_1/crazyflie_2_1-datasheet.pdf">Bitcraze Crazyflie 2.x</a> nanoquadrotor.</p>
</section>
<section id="marsexplorer-environments-for-controlling-robot-on-mars">
<h3><a class="reference external" href="https://github.com/dimikout3/MarsExplorer">MarsExplorer: Environments for controlling robot on Mars</a><a class="headerlink" href="#marsexplorer-environments-for-controlling-robot-on-mars" title="Permalink to this heading">#</a></h3>
<p>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.</p>
</section>
<section id="robo-gym-real-world-and-simulation-robotics">
<h3><a class="reference external" href="https://github.com/jr-robotics/robo-gym">robo-gym: Real-world and simulation robotics</a><a class="headerlink" href="#robo-gym-real-world-and-simulation-robotics" title="Permalink to this heading">#</a></h3>
<p>Robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real-world robotics.</p>
</section>
<section id="offworld-gym">
<h3><a class="reference external" href="https://github.com/offworld-projects/offworld-gym">Offworld-gym</a><a class="headerlink" href="#offworld-gym" title="Permalink to this heading">#</a></h3>
<p>Gym environments that let you control physics robotics in a laboratory via the internet.</p>
</section>
<section id="safe-control-gym">
<h3><a class="reference external" href="https://github.com/utiasDSL/safe-control-gym">safe-control-gym</a><a class="headerlink" href="#safe-control-gym" title="Permalink to this heading">#</a></h3>
<p>PyBullet based CartPole and Quadrotor environments—with <a class="reference external" href="https://web.casadi.org">CasADi</a> (symbolic) <em>a priori</em> dynamics and constraints—for learning-based control and model-based reinforcement learning.</p>
</section>
<section id="gym-softrobot-soft-robotics-environments">
<h3><a class="reference external" href="https://github.com/skim0119/gym-softrobot/">gym-softrobot: Soft-robotics environments</a><a class="headerlink" href="#gym-softrobot-soft-robotics-environments" title="Permalink to this heading">#</a></h3>
<p>A large-scale benchmark for co-optimizing the design and control of soft robots.</p>
</section>
<section id="igibson">
<h3><a class="reference external" href="https://github.com/StanfordVL/iGibson/">iGibson</a><a class="headerlink" href="#igibson" title="Permalink to this heading">#</a></h3>
<p>A Simulation Environment to train Robots in Large Realistic Interactive Scenes</p>
</section>
<section id="dexteroushands-dual-dexterous-hand-manipulation-tasks">
<h3><a class="reference external" href="https://github.com/PKU-MARL/DexterousHands/">DexterousHands: dual dexterous hand manipulation tasks</a><a class="headerlink" href="#dexteroushands-dual-dexterous-hand-manipulation-tasks" title="Permalink to this heading">#</a></h3>
<p>This is a library that provides dual dexterous hand manipulation tasks through Isaac Gym</p>
</section>
<section id="omniisaacgymenvs">
<h3><a class="reference external" href="https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/">OmniIsaacGymEnvs</a><a class="headerlink" href="#omniisaacgymenvs" title="Permalink to this heading">#</a></h3>
<p>Reinforcement Learning Environments for Omniverse Isaac Gym</p>
</section>
</section>
<section id="autonomous-driving-environments">
<h2>Autonomous Driving environments<a class="headerlink" href="#autonomous-driving-environments" title="Permalink to this heading">#</a></h2>
<section id="gym-duckietown">
<h3><a class="reference external" href="https://github.com/duckietown/gym-duckietown">gym-duckietown</a><a class="headerlink" href="#gym-duckietown" title="Permalink to this heading">#</a></h3>
<p>A lane-following simulator built for the <a class="reference external" href="http://duckietown.org/">Duckietown</a> project (small-scale self-driving car course).</p>
</section>
<section id="gym-electric-motor">
<h3><a class="reference external" href="https://github.com/upb-lea/gym-electric-motor">gym-electric-motor</a><a class="headerlink" href="#gym-electric-motor" title="Permalink to this heading">#</a></h3>
<p>An environment for simulating a wide variety of electric drives taking into account different types of electric motors and converters. Control schemes can be continuous, yielding a voltage duty cycle, or discrete, determining converter switching states directly.</p>
</section>
<section id="commonroad-rl">
<h3><a class="reference external" href="https://commonroad.in.tum.de/tools/commonroad-rl">CommonRoad-RL</a><a class="headerlink" href="#commonroad-rl" title="Permalink to this heading">#</a></h3>
<p>A Gym for solving motion planning problems for various traffic scenarios compatible with <a class="reference external" href="https://commonroad.in.tum.de/scenarios">CommonRoad benchmarks</a>, which provides configurable rewards, action spaces, and observation spaces.</p>
</section>
<section id="racing-dreamer">
<h3><a class="reference external" href="https://github.com/CPS-TUWien/racing_dreamer/">racing_dreamer</a><a class="headerlink" href="#racing-dreamer" title="Permalink to this heading">#</a></h3>
<p>Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing</p>
</section>
<section id="l2r-multimodal-control-environment-where-agents-learn-how-to-race">
<h3><a class="reference external" href="https://github.com/learn-to-race/l2r/">l2r: Multimodal control environment where agents learn how to race</a><a class="headerlink" href="#l2r-multimodal-control-environment-where-agents-learn-how-to-race" title="Permalink to this heading">#</a></h3>
<p>An open-source reinforcement learning environment for autonomous racing.</p>
</section>
<section id="racecar-gym">
<h3><a class="reference external" href="https://github.com/axelbr/racecar_gym/">racecar_gym</a><a class="headerlink" href="#racecar-gym" title="Permalink to this heading">#</a></h3>
<p>A gym environment for a miniature racecar using the pybullet physics engine.</p>
</section>
</section>
<section id="other-environments">
<h2>Other environments<a class="headerlink" href="#other-environments" title="Permalink to this heading">#</a></h2>
<section id="compilergym-optimise-compiler-tasks">
<h3><a class="reference external" href="https://github.com/facebookresearch/CompilerGym">CompilerGym: Optimise compiler tasks</a><a class="headerlink" href="#compilergym-optimise-compiler-tasks" title="Permalink to this heading">#</a></h3>
<p>Reinforcement learning environments for compiler optimization tasks, such as LLVM phase ordering, GCC flag tuning, and CUDA loop nest code generation.</p>
</section>
<section id="carl-context-adaptive-rl">
<h3><a class="reference external" href="https://github.com/automl/CARL">CARL: context adaptive RL</a><a class="headerlink" href="#carl-context-adaptive-rl" title="Permalink to this heading">#</a></h3>
<p>Configurable reinforcement learning environments for testing generalization, e.g. CartPole with variable pole lengths or Brax robots with different ground frictions.</p>
</section>
<section id="gym-cellular-automata-cellular-automata-environments">
<h3><a class="reference external" href="https://github.com/elbecerrasoto/gym-cellular-automata">gym-cellular-automata: Cellular Automata environments</a><a class="headerlink" href="#gym-cellular-automata-cellular-automata-environments" title="Permalink to this heading">#</a></h3>
<p>Environments where the agent interacts with <em>Cellular Automata</em> by changing its cell states.</p>
</section>
<section id="gym-sokoban-2d-transportation-puzzles">
<h3><a class="reference external" href="https://github.com/mpSchrader/gym-sokoban">gym-sokoban: 2D Transportation Puzzles</a><a class="headerlink" href="#gym-sokoban-2d-transportation-puzzles" title="Permalink to this heading">#</a></h3>
<p>The environment consists of transportation puzzles in which the player’s goal is to push all boxes to the warehouse’s storage locations. The advantage of the environment is that it generates a new random level every time it is initialized or reset, which prevents overfitting to predefined levels.</p>
</section>
<section id="dacbench-benchmark-library-for-dynamic-algorithm-configuration">
<h3><a class="reference external" href="https://github.com/automl/DACBench">DACBench: Benchmark Library for Dynamic Algorithm configuration</a><a class="headerlink" href="#dacbench-benchmark-library-for-dynamic-algorithm-configuration" title="Permalink to this heading">#</a></h3>
<p>Environments for hyperparameter configuration using RL. Includes cheap surrogate benchmarks as well as real-world algorithms from e.g. AI Planning, Evolutionary Computation and Deep Learning.</p>
</section>
<section id="nlpgym-a-toolkit-to-develop-rl-agents-to-solve-nlp-tasks">
<h3><a class="reference external" href="https://github.com/rajcscw/nlp-gym">NLPGym: A toolkit to develop RL agents to solve NLP tasks</a><a class="headerlink" href="#nlpgym-a-toolkit-to-develop-rl-agents-to-solve-nlp-tasks" title="Permalink to this heading">#</a></h3>
<p><a class="reference external" href="https://arxiv.org/pdf/2011.08272v1.pdf">NLPGym</a> provides interactive environments for standard NLP tasks such as sequence tagging, question answering, and sequence classification. Users can easily customize the tasks with their datasets, observations, features and reward functions.</p>
</section>
<section id="gym-saturation-environments-used-to-prove-theorems">
<h3><a class="reference external" href="https://github.com/inpefess/gym-saturation">gym-saturation: Environments used to prove theorems</a><a class="headerlink" href="#gym-saturation-environments-used-to-prove-theorems" title="Permalink to this heading">#</a></h3>
<p>An environment for guiding automated theorem provers based on saturation algorithms (e.g. <a class="reference external" href="https://github.com/vprover/vampire">Vampire</a>).</p>
</section>
<section id="shinrl-environments-for-evaluating-rl-algorithms">
<h3><a class="reference external" href="https://github.com/omron-sinicx/ShinRL/">ShinRL: Environments for evaluating RL algorithms</a><a class="headerlink" href="#shinrl-environments-for-evaluating-rl-algorithms" title="Permalink to this heading">#</a></h3>
<p>ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives (Deep RL Workshop 2021)</p>
</section>
<section id="gymnax-hardware-accelerated-rl-environments">
<h3><a class="reference external" href="https://github.com/RobertTLange/gymnax/">gymnax: Hardware Accelerated RL Environments</a><a class="headerlink" href="#gymnax-hardware-accelerated-rl-environments" title="Permalink to this heading">#</a></h3>
<p>RL Environments in JAX which allows for highly vectorised environments with support for a number of environments, Gym, MinAtari, bsuite and more.</p>
</section>
<section id="gym-anytrading-financial-trading-environments-for-forex-and-stocks">
<h3><a class="reference external" href="https://github.com/AminHP/gym-anytrading">gym-anytrading: Financial trading environments for FOREX and STOCKS</a><a class="headerlink" href="#gym-anytrading-financial-trading-environments-for-forex-and-stocks" title="Permalink to this heading">#</a></h3>
<p>AnyTrading is a collection of Gym environments for reinforcement learning-based trading algorithms with a great focus on simplicity, flexibility, and comprehensiveness.</p>
</section>
<section id="gym-mtsim-financial-trading-for-metatrader-5-platform">
<h3><a class="reference external" href="https://github.com/AminHP/gym-mtsim">gym-mtsim: Financial trading for MetaTrader 5 platform</a><a class="headerlink" href="#gym-mtsim-financial-trading-for-metatrader-5-platform" title="Permalink to this heading">#</a></h3>
<p>MtSim is a simulator for the MetaTrader 5 trading platform for reinforcement learning-based trading algorithms. MetaTrader 5 is a multi-asset platform that allows trading Forex, Stocks, Crypto, and Futures.</p>
</section>
<section id="openmodelica-microgrid-gym-environments-for-controlling-power-electronic-converters-in-microgrids">
<h3><a class="reference external" href="https://github.com/upb-lea/openmodelica-microgrid-gym">openmodelica-microgrid-gym: Environments for controlling power electronic converters in microgrids</a><a class="headerlink" href="#openmodelica-microgrid-gym-environments-for-controlling-power-electronic-converters-in-microgrids" title="Permalink to this heading">#</a></h3>
<p>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.</p>
</section>
<section id="mobile-env-environments-for-coordination-of-wireless-mobile-networks">
<h3><a class="reference external" href="https://github.com/stefanbschneider/mobile-env/">mobile-env: Environments for coordination of wireless mobile networks</a><a class="headerlink" href="#mobile-env-environments-for-coordination-of-wireless-mobile-networks" title="Permalink to this heading">#</a></h3>
<p>An open, minimalist Gym environment for autonomous coordination in wireless mobile networks.</p>
</section>
<section id="gymfc-a-flight-control-tuning-and-training-framework">
<h3><a class="reference external" href="https://github.com/wil3/gymfc/">GymFC: A flight control tuning and training framework</a><a class="headerlink" href="#gymfc-a-flight-control-tuning-and-training-framework" title="Permalink to this heading">#</a></h3>
<p>GymFC is a modular framework for synthesizing neuro-flight controllers. The architecture integrates digital twinning concepts to provide a seamless transfer of trained policies to hardware. The environment has been used to generate policies for the world’s first open-source neural network flight control firmware <a class="reference external" href="https://github.com/wil3/neuroflight">Neuroflight</a>.</p>
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<li><a class="reference internal" href="#">Third-Party Environments</a><ul>
<li><a class="reference internal" href="#highway-env-autonomous-driving-and-tactical-decision-making-tasks">highway-env: Autonomous driving and tactical decision-making tasks</a></li>
<li><a class="reference internal" href="#sumo-rl-reinforcement-learning-using-sumo-traffic-simulator">sumo-rl: Reinforcement Learning using SUMO traffic simulator</a></li>
<li><a class="reference internal" href="#panda-gym-robotics-environments-using-the-pybullet-physics-engine">panda-gym: Robotics environments using the PyBullet physics engine</a></li>
<li><a class="reference internal" href="#tmrl-trackmania-2020-through-rl">tmrl: TrackMania 2020 through RL</a></li>
<li><a class="reference internal" href="#safety-gymnasium-ensuring-safety-in-real-world-rl-scenarios">Safety-Gymnasium: Ensuring safety in real-world RL scenarios</a></li>
<li><a class="reference internal" href="#stable-retro-classic-retro-games-a-maintained-version-of-openai-retro">stable-retro: Classic retro games, a maintained version of OpenAI Retro</a></li>
<li><a class="reference internal" href="#flappy-bird-gymnasium-a-flappy-bird-environment-for-gymnasium">flappy-bird-gymnasium: A Flappy Bird environment for Gymnasium</a></li>
<li><a class="reference internal" href="#matrix-mdp-easily-create-discrete-mdps">matrix-mdp: Easily create discrete MDPs</a></li>
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<li><a class="reference internal" href="#third-party-environments-using-gym">Third-Party Environments using Gym</a><ul>
<li><a class="reference internal" href="#video-game-environments">Video Game environments</a></li>
<li><a class="reference internal" href="#robotics-environments">Robotics environments</a></li>
<li><a class="reference internal" href="#autonomous-driving-environments">Autonomous Driving environments</a></li>
<li><a class="reference internal" href="#other-environments">Other environments</a></li>
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const versioningConfig = {
githubUser: 'Farama-Foundation',
githubRepo: 'Gymnasium',
};
fetch('/main/_static/versioning/versioning_menu.html').then(response => {
if (response.status === 200) {
response.text().then(text => {
const container = document.createElement("div");
container.innerHTML = text;
document.querySelector("body").appendChild(container);
// innerHtml doenst evaluate scripts, we need to add them dynamically
Array.from(container.querySelectorAll("script")).forEach(oldScript => {
const newScript = document.createElement("script");
Array.from(oldScript.attributes).forEach(attr => newScript.setAttribute(attr.name, attr.value));
newScript.appendChild(document.createTextNode(oldScript.innerHTML));
oldScript.parentNode.replaceChild(newScript, oldScript);
});
});
} else {
console.warn("Unable to load versioning menu", response);
}
});
</script>
</body>
</html>
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