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<p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../introduction/basic_usage/">Basic Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../introduction/train_agent/">Training an Agent</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="third-party-environments">
<h1>Third-Party Environments<a class="headerlink" href="#third-party-environments" title="Link 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>
<section id="third-party-environments-with-gymnasium">
<h2>Third-party environments with Gymnasium<a class="headerlink" href="#third-party-environments-with-gymnasium" title="Link to this heading">#</a></h2>
<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/bnJ6kubTg6">Discord</a>.</em></p>
<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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/automl/carl" /></p>
<p>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.</p>
</section>
<section id="dacbench-a-benchmark-for-dynamic-algorithm-configuration">
<h3><a class="reference external" href="https://github.com/automl/DACBench">DACBench: a benchmark for Dynamic Algorithm Configuration</a><a class="headerlink" href="#dacbench-a-benchmark-for-dynamic-algorithm-configuration" title="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.3-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/automl/DACBench" /></p>
<p>A benchmark library for <a class="reference external" href="https://www.automl.org/dynamic-algorithm-configuration/">Dynamic Algorithm Configuration</a>. Its focus is on reproducibility and comparability of different DAC methods as well as easy analysis of the optimization process.</p>
</section>
<section id="flappy-bird-env">
<h3><a class="reference external" href="https://github.com/robertoschiavone/flappy-bird-env">flappy-bird-env</a><a class="headerlink" href="#flappy-bird-env" title="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/robertoschiavone/flappy-bird-env" /></p>
<p>Flappy Bird as a Farama Gymnasium environment.</p>
</section>
<section id="flappy-bird-gymnasium-a-flappy-bird-environment-for-gymnasium">
<h3><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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/markub3327/flappy-bird-gymnasium" /></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="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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/elbecerrasoto/gym-cellular-automata" /></p>
<p>Environments where the agent interacts with <em>Cellular Automata</em> by changing its cell states.</p>
</section>
<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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.0-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/duburcqa/jiminy" /></p>
<p>gym-jiminy presents an extension of the initial Gym for robotics using <a class="reference external" href="https://github.com/duburcqa/jiminy">Jiminy</a>, 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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/JacopoPan/gym-pybullet-drones" /></p>
<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="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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/inpefess/gym-saturation" /></p>
<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="gym-trading-env-trading-environment">
<h3><a class="reference external" href="https://gym-trading-env.readthedocs.io/">gym-trading-env: Trading Environment</a><a class="headerlink" href="#gym-trading-env-trading-environment" title="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
<img alt="Github stars" src="https://img.shields.io/github/stars/ClementPerroud/Gym-Trading-Env" /></p>
<p>Gym Trading Env simulates stock (or crypto) market from historical data. It was designed to be fast and easily customizable.</p>
</section>
<section id="highway-env-autonomous-driving-and-tactical-decision-making-tasks">
<h3><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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/eleurent/highway-env" /></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="matrix-mdp-easily-create-discrete-mdps">
<h3><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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.2-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/Paul-543NA/matrix-mdp-gym" /></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 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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/stefanbschneider/mobile-env" /></p>
<p>An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.</p>
</section>
<section id="panda-gym-robotics-environments-using-the-pybullet-physics-engine">
<h3><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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.3-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/qgallouedec/panda-gym" /></p>
<p>PyBullet based simulations of a robotic arm moving objects.</p>
</section>
<section id="pystk2-gymnasium-supertuxkart-races-gymnasium-wrapper">
<h3><a class="reference external" href="https://github.com/bpiwowar/pystk2-gymnasium">pystk2-gymnasium: SuperTuxKart races gymnasium wrapper</a><a class="headerlink" href="#pystk2-gymnasium-supertuxkart-races-gymnasium-wrapper" title="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.29.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/bpiwowar/pystk2-gymnasium" /></p>
<p>Uses a <a class="reference external" href="https://github.com/bpiwowar/pystk2">python wrapper</a> around <a class="reference external" href="https://supertuxkart.net/fr/Main_Page">SuperTuxKart</a> that allows to access the world state and to control a race.</p>
</section>
<section id="qwop-an-environment-for-bennet-foddy-s-game-qwop">
<h3><a class="reference external" href="https://github.com/smanolloff/qwop-gym">QWOP: An environment for Bennet Foddy’s game QWOP</a><a class="headerlink" href="#qwop-an-environment-for-bennet-foddy-s-game-qwop" title="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.29.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/smanolloff/qwop-gym" /></p>
<p>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).</p>
</section>
<section id="safety-gymnasium-ensuring-safety-in-real-world-rl-scenarios">
<h3><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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/PKU-MARL/safety-gymnasium" /></p>
<p>Highly scalable and customizable Safe Reinforcement Learning library.</p>
</section>
<section id="simplegrid-a-simple-grid-environment-for-gymnasium">
<h3><a class="reference external" href="https://github.com/damat-le/gym-simplegrid">SimpleGrid: a simple grid environment for Gymnasium</a><a class="headerlink" href="#simplegrid-a-simple-grid-environment-for-gymnasium" title="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/damat-le/gym-simplegrid" /></p>
<p>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.</p>
</section>
<section id="spark-sched-sim-environments-for-scheduling-dag-jobs-in-apache-spark">
<h3><a class="reference external" href="https://github.com/ArchieGertsman/spark-sched-sim">spark-sched-sim: Environments for scheduling DAG jobs in Apache Spark</a><a class="headerlink" href="#spark-sched-sim-environments-for-scheduling-dag-jobs-in-apache-spark" title="Link to this heading">#</a></h3>
<p>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.</p>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.29.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/ArchieGertsman/spark-sched-sim" /></p>
</section>
<section id="stable-retro-classic-retro-games-a-maintained-version-of-openai-retro">
<h3><a class="reference external" href="https://github.com/Farama-Foundation/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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/Farama-Foundation/stable-retro" /></p>
<p>Supported fork of <a class="reference external" href="https://openai.com/research/gym-retro">gym-retro</a>: turn classic video games into Gymnasium environments.</p>
</section>
<section id="sumo-rl-reinforcement-learning-using-sumo-traffic-simulator">
<h3><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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.3-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/LucasAlegre/sumo-rl" /></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="tmrl-trackmania-2020-through-rl">
<h3><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="Link to this heading">#</a></h3>
<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
<img alt="GitHub stars" src="https://img.shields.io/github/stars/trackmania-rl/tmrl" /></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>
<section id="third-party-environments-using-gym">
<h2>Third-Party Environments using Gym<a class="headerlink" href="#third-party-environments-using-gym" title="Link to this heading">#</a></h2>
<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>
<section id="video-game-environments">
<h2>Video Game environments<a class="headerlink" href="#video-game-environments" title="Link 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="Link to this heading">#</a></h3>
<p>A 3v3 MOBA environment where you train creatures to fight each other.</p>
</section>
<section id="slimevolleygym-a-simple-environment-for-slime-volleyball-game">
<h3><a class="reference external" href="https://github.com/hardmaru/slimevolleygym">SlimeVolleyGym: A simple environment for Slime Volleyball game</a><a class="headerlink" href="#slimevolleygym-a-simple-environment-for-slime-volleyball-game" title="Link 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.</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="Link to this heading">#</a></h3>
<p>Gym (and PettingZoo) 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="Link to this heading">#</a></h3>
<p>Uses The <a class="reference external" href="https://www.o3de.org/">Open 3D Engine</a> for AI simulations and can interoperate with the Gym. Uses <a class="reference external" href="https://github.com/bulletphysics/bullet3">PyBullet</a> physics.</p>
</section>
</section>
<section id="robotics-environments">
<h2>Robotics environments<a class="headerlink" href="#robotics-environments" title="Link to this heading">#</a></h2>
<section id="pyflyt-uav-flight-simulator-environments-for-reinforcement-learning-research">
<h3><a class="reference external" href="https://jjshoots.github.io/PyFlyt/index.html#">PyFlyt: UAV Flight Simulator Environments for Reinforcement Learning Research</a><a class="headerlink" href="#pyflyt-uav-flight-simulator-environments-for-reinforcement-learning-research" title="Link to this heading">#</a></h3>
<p>A library for testing reinforcement learning algorithms on various UAVs.
It is built on the <a class="reference external" href="https://github.com/bulletphysics/bullet3">Bullet</a> 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.</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="Link 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="Link 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-control-real-robots-remotely-for-free">
<h3><a class="reference external" href="https://github.com/offworld-projects/offworld-gym">Offworld-gym: Control real robots remotely for free</a><a class="headerlink" href="#offworld-gym-control-real-robots-remotely-for-free" title="Link to this heading">#</a></h3>
<p>Gym environments that let you control real robots in a laboratory via the internet.</p>
</section>
<section id="safe-control-gym-evaluate-safety-of-rl-algorithms">
<h3><a class="reference external" href="https://github.com/utiasDSL/safe-control-gym">safe-control-gym: Evaluate safety of RL algorithms</a><a class="headerlink" href="#safe-control-gym-evaluate-safety-of-rl-algorithms" title="Link to this heading">#</a></h3>
<p>Evaluate safety, robustness and generalization via 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.</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="Link 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-photorealistic-and-interactive-robotics-environments">
<h3><a class="reference external" href="https://github.com/StanfordVL/iGibson/">iGibson: Photorealistic and interactive robotics environments</a><a class="headerlink" href="#igibson-photorealistic-and-interactive-robotics-environments" title="Link to this heading">#</a></h3>
<p>A simulation environment with high-quality realistic scenes, with interactive physics using <a class="reference external" href="https://github.com/bulletphysics/bullet3">PyBullet</a>.</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="Link 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-gym-environments-for-nvidia-omniverse-isaac">
<h3><a class="reference external" href="https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/">OmniIsaacGymEnvs: Gym environments for NVIDIA Omniverse Isaac </a><a class="headerlink" href="#omniisaacgymenvs-gym-environments-for-nvidia-omniverse-isaac" title="Link to this heading">#</a></h3>
<p>Reinforcement Learning Environments for <a class="reference external" href="https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html">Omniverse Isaac simulator</a>.</p>
</section>
</section>
<section id="autonomous-driving-environments">
<h2>Autonomous Driving environments<a class="headerlink" href="#autonomous-driving-environments" title="Link to this heading">#</a></h2>
<section id="gym-duckietown-lane-following-for-self-driving-cars">
<h3><a class="reference external" href="https://github.com/duckietown/gym-duckietown">gym-duckietown: Lane-following for self-driving cars</a><a class="headerlink" href="#gym-duckietown-lane-following-for-self-driving-cars" title="Link 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-gym-environments-for-electric-motor-simulations">
<h3><a class="reference external" href="https://github.com/upb-lea/gym-electric-motor">gym-electric-motor: Gym environments for electric motor simulations</a><a class="headerlink" href="#gym-electric-motor-gym-environments-for-electric-motor-simulations" title="Link 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.</p>
</section>
<section id="commonroad-rl-motion-planning-for-traffic-scenarios">
<h3><a class="reference external" href="https://commonroad.in.tum.de/tools/commonroad-rl">CommonRoad-RL: Motion planning for traffic scenarios </a><a class="headerlink" href="#commonroad-rl-motion-planning-for-traffic-scenarios" title="Link 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-latent-imagination-in-autonomous-racing">
<h3><a class="reference external" href="https://github.com/CPS-TUWien/racing_dreamer/">racing_dreamer: Latent imagination in autonomous racing</a><a class="headerlink" href="#racing-dreamer-latent-imagination-in-autonomous-racing" title="Link to this heading">#</a></h3>
<p>Train a model-based RL agent in simulation and, without finetuning, transfer it to small-scale race cars.</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="Link to this heading">#</a></h3>
<p>An open-source reinforcement learning environment for autonomous racing.</p>
</section>
<section id="racecar-gym-miniature-racecar-env-using-pybullet">
<h3><a class="reference external" href="https://github.com/axelbr/racecar_gym/">racecar_gym: Miniature racecar env using PyBullet</a><a class="headerlink" href="#racecar-gym-miniature-racecar-env-using-pybullet" title="Link to this heading">#</a></h3>
<p>A gym environment for a miniature racecar using the <a class="reference external" href="https://github.com/bulletphysics/bullet3">PyBullet</a> physics engine.</p>
</section>
</section>
<section id="other-environments">
<h2>Other environments<a class="headerlink" href="#other-environments" title="Link to this heading">#</a></h2>
<section id="connect-4-gym-an-environment-for-practicing-self-playing">
<h3><a class="reference external" href="https://github.com/lucasBertola/Connect-4-Gym-env-Reinforcement-learning">Connect-4-gym : An environment for practicing self playing</a><a class="headerlink" href="#connect-4-gym-an-environment-for-practicing-self-playing" title="Link to this heading">#</a></h3>
<p>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.</p>
</section>
<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="Link 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="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="Link 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.</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="Link 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.</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="Link 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="Link 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="Link 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="Link to this heading">#</a></h3>
<p>MtSim is a simulator for the <a class="reference external" href="https://www.metatrader5.com/">MetaTrader 5</a> trading platform for reinforcement learning-based trading algorithms.</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="Link 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="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="Link to this heading">#</a></h3>
<p>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 <a class="reference external" href="https://github.com/wil3/neuroflight">Neuroflight</a>.</p>
</section>
</section>
</section>
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<li><a class="reference internal" href="#">Third-Party Environments</a><ul>
<li><a class="reference internal" href="#third-party-environments-with-gymnasium">Third-party environments with Gymnasium</a></li>
<li><a class="reference internal" href="#third-party-environments-using-gym">Third-Party Environments using Gym</a></li>
<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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