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Update third_party_environments.md
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# Third-Party Environments
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Many environments that comply with the Gymnasium API are now maintained under the Farama Foundation's [projects](https://farama.org/projects), along with Gymnasium itself. These include many of the most popular environments using the Gymnasium API, and we encourage you to check them out. This page exclusively lists interesting third party environments that are not part of the Farama Foundation.
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## Video Game Environments
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### [ViZDoom](https://github.com/mwydmuch/ViZDoom)
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An environment centered around the original [Doom](https://en.wikipedia.org/wiki/Doom_(1993_video_game)) game, focusing on visual control (from image to actions) at thousands of frames per second. ViZDoom supports depth and automatic annotation/labels buffers, as well as accessing the sound. The Gym wrappers provide easy-to-use access to the example scenarios that come with ViZDoom. Since 2016, the [ViZDoom paper](https://arxiv.org/abs/1605.02097) has been cited more than 600 times.
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### [ flappy-bird-gym: A Flappy Bird environment for OpenAI Gym](https://github.com/Talendar/flappy-bird-gym)
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### [ flappy-bird-gym: A Flappy Bird environment for Gym](https://github.com/Talendar/flappy-bird-gym)
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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.
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@@ -14,14 +13,6 @@ A simple environment for single-agent reinforcement learning algorithms on a clo
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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
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### [ MineRL](https://github.com/minerllabs/minerl)
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Gym interface with Minecraft game focused on a specific sparse reward challenge
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### [ Procgen](https://github.com/openai/procgen)
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16 simple-to-use procedurally-generated gym environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills. The environments run at high speed (thousands of steps per second) on a single core.
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### [ SlimeVolleyGym: A simple environment for single and multi-agent reinforcement learning](https://github.com/hardmaru/slimevolleygym)
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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.
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@@ -34,75 +25,107 @@ Supported fork of gym-retro with additional games, states, scenarios, etc. Open
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Gym wrappers for arbitrary and premade environments with the Unity game engine.
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## Classic Environments (board, card, etc. games)
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### [ gym-games](https://github.com/qlan3/gym-games)
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### [ gym-abalone: A two-player abstract strategy board game](https://github.com/towzeur/gym-abalone)
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Gym implementations of the MinAtar games, various PyGame Learning Environment games, and various custom exploration games
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An implementation of the board game Abalone.
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### [ PGE: Parallel Game Engine](https://github.com/222464/PGE)
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### [ gym-spoof](https://github.com/MouseAndKeyboard/gym-spoof)
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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.
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Spoof, otherwise known as "The 3-coin game", is a multi-agent (2-player), imperfect-information, zero-sum game.
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### [ gym-xiangqi: Xiangqi - The Chinese Chess Game](https://github.com/tanliyon/gym-xiangqi)
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A reinforcement learning environment of Xiangqi, the Chinese Chess game.
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### [ RubiksCubeGym](https://github.com/DoubleGremlin181/RubiksCubeGym)
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The RubiksCubeGym package provides environments for twisty puzzles with multiple reward functions to help simulate the methods used by humans.
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### [ GymGo](https://github.com/aigagror/GymGo)
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The board game Go, also known as Weiqi. The game that was famously conquered by AlphaGo.
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## Robotics Environments
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### [ GymFC: A flight control tuning and training framework](https://github.com/wil3/gymfc/)
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### [GymFC: A flight control tuning and training framework](https://github.com/wil3/gymfc/)
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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 OpenAI environment has been used to generate policies for the world's first open-source neural network flight control firmware [Neuroflight](https://github.com/wil3/neuroflight).
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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 [Neuroflight](https://github.com/wil3/neuroflight).
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### [ gym-gazebo](https://github.com/erlerobot/gym-gazebo/)
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### [gym-gazebo](https://github.com/erlerobot/gym-gazebo/)
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gym-gazebo presents an extension of the initial OpenAI gym for robotics using ROS and Gazebo, an advanced 3D modeling and
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gym-gazebo presents an extension of the initial Gym for robotics using ROS and Gazebo, an advanced 3D modeling and
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rendering tool.
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### [ gym-goddard: Goddard's Rocket Problem](https://github.com/osannolik/gym-goddard)
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### [gym-goddard: Goddard's Rocket Problem](https://github.com/osannolik/gym-goddard)
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An environment for simulating the classical optimal control problem where the thrust of a vertically ascending rocket shall be determined such that it reaches the maximum possible altitude while being subject to varying aerodynamic drag, gravity, and mass.
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### [ gym-jiminy: training Robots in Jiminy](https://github.com/Wandercraft/jiminy)
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### [gym-jiminy: training Robots in Jiminy](https://github.com/Wandercraft/jiminy)
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gym-jiminy presents an extension of the initial OpenAI 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.
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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.
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### [ gym-miniworld](https://github.com/maximecb/gym-miniworld)
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MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research. It can be used to simulate environments with rooms, doors, hallways and various objects (eg: office and home environments, mazes). MiniWorld can be seen as an alternative to VizDoom or DMLab. It is written 100% in Python and designed to be easily modified or extended.
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### [ gym-pybullet-drones](https://github.com/JacopoPan/gym-pybullet-drones)
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### [gym-pybullet-drones](https://github.com/JacopoPan/gym-pybullet-drones)
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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.
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### [ MarsExplorer](https://github.com/dimikout3/MarsExplorer)
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### [MarsExplorer](https://github.com/dimikout3/MarsExplorer)
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Mars Explorer is an openai-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.
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Mars Explorer is a Ggym 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.
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### [ panda-gym ](https://github.com/qgallouedec/panda-gym/)
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### [panda-gym ](https://github.com/qgallouedec/panda-gym/)
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PyBullet based simulations of a robotic arm moving objects.
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### [ PyBullet Robotics Environments](https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit#heading=h.wz5to0x8kqmr)
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3D physics environments like the Mujoco environments but uses the Bullet physics engine and do not require a commercial license. Works on Mac/Linux/Windows.
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### [ robo-gym](https://github.com/jr-robotics/robo-gym)
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### [robo-gym](https://github.com/jr-robotics/robo-gym)
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robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real-world robotics.
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### [ Offworld-gym](https://github.com/offworld-projects/offworld-gym)
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### [Offworld-gym](https://github.com/offworld-projects/offworld-gym)
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Gym environments that let you control physics robotics in a laboratory via the internet.
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### [osim-rl](https://github.com/stanfordnmbl/osim-rl)
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Musculoskeletal Models in OpenSim. A human musculoskeletal model and a physics-based simulation environment where you can synthesize physically and physiologically accurate motion. One of the environments built in this framework is a competition environment for a NIPS 2017 challenge.
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### [safe-control-gym](https://github.com/utiasDSL/safe-control-gym)
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PyBullet based CartPole and Quadrotor environments—with [CasADi](https://web.casadi.org) (symbolic) *a priori* dynamics and constraints—for learning-based control and model-based reinforcement learning.
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### [racecar_gym](https://github.com/axelbr/racecar_gym/)
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A gym environment for a miniature racecar using the pybullet physics engine.
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### [jiminy](https://github.com/duburcqa/jiminy/)
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Jiminy: a fast and portable Python/C++ simulator of poly-articulated systems with Gym interface for reinforcement learning
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### [gym-softrobot](https://github.com/skim0119/gym-softrobot/)
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Softrobotics environment package for Gym
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### [ostrichrl](https://github.com/vittorione94/ostrichrl/)
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This is the repository accompanying the paper [OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion](https://arxiv.org/abs/2112.06061).
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### [quadruped-gym](https://github.com/dtch1997/quadruped-gym/)
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A Gym environment for the training of legged robots
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### [evogym](https://github.com/EvolutionGym/evogym/)
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A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.
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### [iGibson](https://github.com/StanfordVL/iGibson/)
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A Simulation Environment to train Robots in Large Realistic Interactive Scenes
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### [DexterousHands](https://github.com/PKU-MARL/DexterousHands/)
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This is a library that provides dual dexterous hand manipulation tasks through Isaac Gym
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### [OmniIsaacGymEnvs](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/)
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Reinforcement Learning Environments for Omniverse Isaac Gym
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### [SpaceRobotEnv](https://github.com/Tsinghua-Space-Robot-Learning-Group/SpaceRobotEnv/)
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A gym environment designed for free-floating space robot control based on the MuJoCo platform.
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### [gym-line-follower](https://github.com/nplan/gym-line-follower/)
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Line follower robot simulator environment for Open AI Gym.
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## Autonomous Driving and Traffic Control Environments
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### [ gym-carla](https://github.com/cjy1992/gym-carla)
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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.
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### [tmrl](https://github.com/trackmania-rl/tmrl/)
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## Multi-Agents
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TrackMania 2020 through RL
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### [racing_dreamer](https://github.com/CPS-TUWien/racing_dreamer/)
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Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
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### [l2r](https://github.com/learn-to-race/l2r/)
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An open-source reinforcement learning environment for autonomous racing.
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### [gym_torcs](https://github.com/ugo-nama-kun/gym_torcs/)
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Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with gym-like interface. TORCS is the open-source realistic car racing simulator recently used as an RL benchmark task in several AI studies.
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## Recommendation System Environments
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### [ gym-adserve](https://github.com/falox/gym-adserver)
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An environment that implements a typical [multi-armed bandit scenario](https://en.wikipedia.org/wiki/Multi-armed_bandit) where an [ad server](https://en.wikipedia.org/wiki/Ad_serving) must select the best advertisement to be displayed in a web page. Some example agents included: Random, epsilon-Greedy, Softmax, and UCB1.
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### [ gym-recsys](https://github.com/zuoxingdong/gym-recsys)
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This package describes an Gym interface for creating a simulation environment of reinforcement learning-based recommender systems (RL-RecSys). The design strives for simple and flexible APIs to support novel research.
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### [ VirtualTaobao](https://github.com/eyounx/VirtualTaobao/)
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An environment for online recommendations, where customers are learned from Taobao.com, one of the world's largest e-commerce platforms.
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## Industrial Process Environments
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### [ gym-inventory](https://github.com/paulhendricks/gym-inventory)
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gym-inventory is a single-agent domain featuring discrete state and action spaces that an AI agent might encounter in inventory control problems.
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### [ openmodelica-microgrid-gym](https://github.com/upb-lea/openmodelica-microgrid-gym)
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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.
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### [mobile-env](https://github.com/stefanbschneider/mobile-env/)
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An open, minimalist Gym environment for autonomous coordination in wireless mobile networks.
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### [PyElastica](https://github.com/GazzolaLab/PyElastica/)
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Python implementation of Elastica, open-source software for the simulation of assemblies of slender, one-dimensional structures using Cosserat Rod theory.
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## Financial Environments
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### [ gym-anytrading](https://github.com/AminHP/gym-anytrading)
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AnyTrading is a collection of Gym environments for reinforcement learning-based trading algorithms with a great focus on simplicity, flexibility, and comprehensiveness.
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### [ gym-mtsim](https://github.com/AminHP/gym-mtsim)
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MtSim is a general-purpose, flexible, an
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### [PettingZoo](https://github.com/Farama-Foundation/PettingZoo)
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PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym.
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## Other Environments
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### [ anomalous_rl_envs](https://github.com/modanesh/anomalous_rl_envs)
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A set of environments from control tasks: Acrobot, CartPole, and LunarLander with various types of anomalies injected into them. It could be very useful to study the behavior and robustness of a policy.
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### [ CARL](https://github.com/automl/CARL)
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Configurable reinforcement learning environments for testing generalization, e.g. CartPole with variable pole lengths or Brax robots with different ground frictions.
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@@ -158,62 +236,24 @@ Reinforcement learning environments for compiler optimization tasks, such as LLV
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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.
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### [ Gridworld](https://github.com/addy1997/Gridworld)
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The Gridworld package provides grid-based environments to help simulate the results for model-based reinforcement learning algorithms. The initial release supports single agent system only. Some features in this version of the software have become obsolete. New features are being added to the software like windygrid environment.
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### [ gym-adserve](https://github.com/falox/gym-adserver)
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An environment that implements a typical [multi-armed bandit scenario](https://en.wikipedia.org/wiki/Multi-armed_bandit) where an [ad server](https://en.wikipedia.org/wiki/Ad_serving) must select the best advertisement to be displayed in a web page. Some example agents included: Random, epsilon-Greedy, Softmax, and UCB1.
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### [ gym-algorithmic](https://github.com/Rohan138/gym-algorithmic)
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These are a variety of algorithmic tasks, such as learning to copy a sequence, present in Gym prior to Gym 0.20.0.
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### [ gym-anytrading](https://github.com/AminHP/gym-anytrading)
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AnyTrading is a collection of OpenAI Gym environments for reinforcement learning-based trading algorithms with a great focus on simplicity, flexibility, and comprehensiveness.
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### [ gym-autokey](https://github.com/Flunzmas/gym-autokey)
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An environment for automated rule-based deductive program verification in the KeY verification system.
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### [ gym-ccc](https://github.com/acxz/gym-ccc)
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Environments that extend gym's classic control and add many new features including continuous action spaces.
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### [ gym-cellular-automata](https://github.com/elbecerrasoto/gym-cellular-automata)
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Environments where the agent interacts with _Cellular Automata_ by changing its cell states.
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### [ gym-games](https://github.com/qlan3/gym-games)
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Gym implementations of the MinAtar games, various PyGame Learning Environment games, and various custom exploration games
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### [ gym-inventory](https://github.com/paulhendricks/gym-inventory)
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gym-inventory is a single-agent domain featuring discrete state and action spaces that an AI agent might encounter in inventory control problems.
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### [ gym-maze](https://github.com/tuzzer/gym-maze/)
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A simple 2D maze environment where an agent finds its way from the start position to the goal.
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### [ gym-mtsim](https://github.com/AminHP/gym-mtsim)
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MtSim is a general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform.
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### [ gym-legacy-toytext](https://github.com/Rohan138/gym-legacy-toytext)
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These are the unused toy-text environments present in Gym prior to Gym 0.20.0.
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d easy-to-use simulator alongside a Gym trading environment for MetaTrader 5 trading platform.
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### [ gym-riverswim](https://github.com/erfanMhi/gym-riverswim)
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A simple environment for benchmarking reinforcement learning exploration techniques in a simplified setting. Hard exploration.
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### [ gym-recsys](https://github.com/zuoxingdong/gym-recsys)
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This package describes an OpenAI Gym interface for creating a simulation environment of reinforcement learning-based recommender systems (RL-RecSys). The design strives for simple and flexible APIs to support novel research.
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### [ gym-sokoban](https://github.com/mpSchrader/gym-sokoban)
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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. 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.
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@@ -234,42 +274,14 @@ The environment is fully compatible with the OpenAI baselines and exposes a NAS
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3D procedurally generated tower where you have to climb to the highest level possible
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### [ openmodelica-microgrid-gym](https://github.com/upb-lea/openmodelica-microgrid-gym)
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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.
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### [ osim-rl](https://github.com/stanfordnmbl/osim-rl)
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Musculoskeletal Models in OpenSim. A human musculoskeletal model and a physics-based simulation environment where you can synthesize physically and physiologically accurate motion. One of the environments built in this framework is a competition environment for a NIPS 2017 challenge.
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### [ PGE: Parallel Game Engine](https://github.com/222464/PGE)
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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.
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### [ QASGym](https://github.com/qdevpsi3/quantum-arch-search)
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This is a list of environments for quantum architecture search following the description in [Quantum Architecture Search via Deep Reinforcement Learning](https://arxiv.org/abs/2104.07715). The agent designs the quantum circuit by taking actions in the environment. Each action corresponds to a gate applied on some wires. The goal is to build a circuit U that generates the target n-qubit quantum state that belongs to the environment and is hidden from the agent. The circuits are built using [Google QuantumAI Cirq](https://quantumai.google/cirq).
|
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### [ safe-control-gym](https://github.com/utiasDSL/safe-control-gym)
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PyBullet based CartPole and Quadrotor environments—with [CasADi](https://web.casadi.org) (symbolic) *a priori* dynamics and constraints—for learning-based control and model-based reinforcement learning.
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||||
|
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### [ VirtualTaobao](https://github.com/eyounx/VirtualTaobao/)
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||||
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||||
An environment for online recommendations, where customers are learned from Taobao.com, one of the world's largest e-commerce platforms.
|
||||
|
||||
### [ mo-gym](https://github.com/LucasAlegre/mo-gym)
|
||||
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||||
Multi-objective RL (MORL) gym environments, where the reward is a NumPy array of different (possibly conflicting) objectives.
|
||||
|
||||
### [ABIDES-Gym](https://github.com/jpmorganchase/abides-jpmc-public)
|
||||
|
||||
ABIDES (Agent-Based Interactive Discrete Event Simulator) is a message-based multi-agent discrete event-based simulator. It enables simulation of complex multi-agent systems for different domains. ABIDES has already supported work in [equity markets simulation](https://arxiv.org/abs/1904.12066) and [federated learning](https://dl.acm.org/doi/abs/10.1145/3383455.3422562).
|
||||
|
||||
[ABIDES-Gym](https://arxiv.org/abs/2110.14771) (ACM-ICAIF21 publication) is a new wrapper built around ABIDES that enables using ABIDES simulator as an Open AI Gym environment for the training of Reinforcement Learning algorithms.
|
||||
|
||||
We apply for this work by specifically using the market's extension of ABIDES/ABIDES-Markets and developing two benchmark financial market Gym environments for training daily investor and execution agents. As a result, these two environments describe classic financial problems with a complex interactive market behavior response to the experimental agent's action.
|
||||
|
||||
### [gym-saturation](https://github.com/inpefess/gym-saturation)
|
||||
|
||||
An environment for guiding automated theorem provers based on saturation algorithms (e.g. [Vampire](https://github.com/vprover/vampire)).
|
||||
@@ -282,109 +294,21 @@ ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Pe
|
||||
|
||||
reinforcement learning for f1tenth racing
|
||||
|
||||
### [go-explore](https://github.com/qgallouedec/go-explore/)
|
||||
### [ RubiksCubeGym](https://github.com/DoubleGremlin181/RubiksCubeGym)
|
||||
|
||||
Unofficial implementation of the Go-Explore algorithm presented in [First return then explore](https://arxiv.org/abs/2004.12919) based on [stable-baselines3](https://github.com/DLR-RM/stable-baselines3).
|
||||
|
||||
### [tmrl](https://github.com/trackmania-rl/tmrl/)
|
||||
|
||||
TrackMania 2020 through RL
|
||||
|
||||
### [racing_dreamer](https://github.com/CPS-TUWien/racing_dreamer/)
|
||||
|
||||
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
|
||||
|
||||
### [racecar_gym](https://github.com/axelbr/racecar_gym/)
|
||||
|
||||
A gym environment for a miniature racecar using the pybullet physics engine.
|
||||
|
||||
### [jiminy](https://github.com/duburcqa/jiminy/)
|
||||
|
||||
Jiminy: a fast and portable Python/C++ simulator of poly-articulated systems with OpenAI Gym interface for reinforcement learning
|
||||
The RubiksCubeGym package provides environments for twisty puzzles with multiple reward functions to help simulate the methods used by humans.
|
||||
|
||||
### [evogym-design-tool](https://github.com/EvolutionGym/evogym-design-tool/)
|
||||
|
||||
Design tool for creating Evolution Gym environments.
|
||||
|
||||
### [l2r](https://github.com/learn-to-race/l2r/)
|
||||
|
||||
An open-source reinforcement learning environment for autonomous racing.
|
||||
|
||||
### [gym_torcs](https://github.com/ugo-nama-kun/gym_torcs/)
|
||||
|
||||
Gym-TORCS is the reinforcement learning (RL) environment in TORCS domain with OpenAI-gym-like interface. TORCS is the open-source realistic car racing simulator recently used as an RL benchmark task in several AI studies.
|
||||
|
||||
### [mobile-env](https://github.com/stefanbschneider/mobile-env/)
|
||||
|
||||
An open, minimalist Gym environment for autonomous coordination in wireless mobile networks.
|
||||
|
||||
### [gym-softrobot](https://github.com/skim0119/gym-softrobot/)
|
||||
|
||||
Softrobotics environment package for OpenAI Gym
|
||||
|
||||
### [PyElastica](https://github.com/GazzolaLab/PyElastica/)
|
||||
|
||||
Python implementation of Elastica, open-source software for the simulation of assemblies of slender, one-dimensional structures using Cosserat Rod theory.
|
||||
|
||||
### [tuxkart-ai](https://github.com/notjedi/tuxkart-ai/)
|
||||
|
||||
RL agent for the SuperTuxKart game.
|
||||
|
||||
### [ostrichrl](https://github.com/vittorione94/ostrichrl/)
|
||||
|
||||
This is the repository accompanying the paper [OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion](https://arxiv.org/abs/2112.06061).
|
||||
|
||||
### [quadruped-gym](https://github.com/dtch1997/quadruped-gym/)
|
||||
|
||||
An OpenAI gym environment for the training of legged robots
|
||||
|
||||
### [Pogo-Stick-Jumping](https://github.com/asalbright/Pogo-Stick-Jumping/)
|
||||
|
||||
OpenAI gym environment, testing and evaluation.
|
||||
|
||||
### [evogym](https://github.com/EvolutionGym/evogym/)
|
||||
|
||||
A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.
|
||||
|
||||
### [iGibson](https://github.com/StanfordVL/iGibson/)
|
||||
|
||||
A Simulation Environment to train Robots in Large Realistic Interactive Scenes
|
||||
|
||||
### [SnakeRL](https://github.com/tboulet/SnakeRL/)
|
||||
|
||||
Repo for Snake RL
|
||||
|
||||
### [starship-landing-gym](https://github.com/Armandpl/starship-landing-gym/)
|
||||
|
||||
A Gym env for propulsive rocket landing.
|
||||
|
||||
### [CompilerGym](https://github.com/facebookresearch/CompilerGym/)
|
||||
|
||||
Reinforcement learning environments for compiler and program optimization tasks
|
||||
A simple Gym environment for propulsive rocket landing
|
||||
|
||||
### [RaveForce](https://github.com/chaosprint/RaveForce/)
|
||||
|
||||
RaveForce - An OpenAI Gym style toolkit for music generation experiments.
|
||||
|
||||
### [gym-line-follower](https://github.com/nplan/gym-line-follower/)
|
||||
|
||||
Line follower robot simulator environment for Open AI Gym.
|
||||
|
||||
### [DexterousHands](https://github.com/PKU-MARL/DexterousHands/)
|
||||
|
||||
This is a library that provides dual dexterous hand manipulation tasks through Isaac Gym
|
||||
|
||||
### [OmniIsaacGymEnvs](https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs/)
|
||||
|
||||
Reinforcement Learning Environments for Omniverse Isaac Gym
|
||||
|
||||
### [border](https://github.com/taku-y/border/)
|
||||
|
||||
A reinforcement learning library in Rust
|
||||
|
||||
### [SpaceRobotEnv](https://github.com/Tsinghua-Space-Robot-Learning-Group/SpaceRobotEnv/)
|
||||
|
||||
A gym environment designed for free-floating space robot control based on the MuJoCo platform.
|
||||
RaveForce - A Gym style toolkit for music generation experiments.
|
||||
|
||||
### [gymnax](https://github.com/RobertTLange/gymnax/)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user