A comprehensive list of categorized reinforcement learning environments.
Started and maintained by Andrew Szot.
Two other resources for RL environments:
Environments are listed alphabetically.
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Assistive-gym
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6 assistive tasks (ScratchItch, BedBathing, Feeding, Drinking, Dressing, and ArmManipulation).
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4 commercial robots (PR2, Jaco, Baxter, Sawyer).
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2 human states: static or active (takes actions according to a separate control policy).
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Customizable female and male human models. 40 actuated human joints (head, torso, arms, waist, and legs).Realistic human joint limit.
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DoorGym
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Train a policy to open up various doors.
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Unity integration.
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Random door knob generator and door knob dataset.
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Gym Gazebo 2
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Toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo.
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Gym Ignition
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Provides the capability of creating reproducible robotics environments for reinforcement learning research.
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Accelerated and multiprocess execution
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IKEA Furniture Assembly
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Complex long-horizon manipulation tasks.
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Includes 80+ furniture models, customizable background, lighting
and textures.
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Features Baxter, Sawyer, and more robots.
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Meta-World
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50 diverse robot manipulation tasks on a simulated Sawyer robotic arm.
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Also includes a variety of evaluation modes varying the number of training and testing tasks.
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Playroom
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Variety of tasks in desk scenario.
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Evaluation code and play dataset will be included soon.
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RAISIM
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Raisim is a physics engine for rigid-body dynamics simulation.
Although it is a general physics engine, it has been mainly
used/tested for robotics and reinforcement learning so far. It
features an efficient implementation of recursive algorithms for
articulated system dynamics (Recursive Newton-Euler and Composite
Rigid Body Algorithm). RaisimLib is an exported cmake package of
raisim.
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RLBench
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100 unique, hand designed tasks.
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Vision-guided manipulation, imitation learning, multi-task
learning, geometric computer vision and few-shot learning.
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Robosuite
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A set of standard benchmarking tasks in robots.
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Defines a framework for easily creating new tasks and environments.
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Roboschool
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Control robots in simulation.
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Can use other physics engines other than MuJoCo.
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Alternative to standard OpenAI Gym mujoco environments.
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Easy to train multiple agents at once.
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Rex-Gym
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OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
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Coin-Run
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Training environment which provides a metric for an agent’s
ability to transfer its experience to novel situations.
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Gym Retro
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Gym Retro lets you turn classic video games into Gym environments
for reinforcement learning and comes with integrations for ~1000.
games.
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Holodeck
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High Fidelity Simulator for Reinforcement Learning and Robotics Research.
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MarLÖ : Reinforcement Learning + Minecraft
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A high level API built on top of Project MalmÖ to facilitate Reinforcement Learning experiments with a great degree of generalizability, capable of solving problems in pseudo-random, procedurally changing single and multi agent environments within the world of the mediatic phenomenon game Minecraft.
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Minecraft
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Data API for the MineRLv0 dataset.
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Also has minecraft environment simulator with basic built in tasks.
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PHYRE
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Benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D enviroment.
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Soccer Simulator
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Can control one or all football players at a time.
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Includes football academy for diverse scenarios such as various
passing scenarios.
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StarCraft 2
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Provides an interface for RL agents to interact with StarCraft 2,
getting observations and sending actions.
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SuperMario
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Gym wrapper for the Super Mario levels. Includes many levels.
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TorchCraft
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Python interface for playing "StarCraft: Brood War".
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VizDoom
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ViZDoom allows developing AI bots that play Doom using only the
visual information (the screen buffer).
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Meta-World
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50 diverse robot manipulation tasks on a simulated Sawyer robotic arm.
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Also includes a variety of evaluation modes varying the number of training and testing tasks.
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Multiworld
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Variety of Gym GoalEnvs that return the goal in the observation.
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Playroom
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Variety of tasks in desk scenario.
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Evaluation code and play dataset will be included soon.
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RLBench
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100 unique, hand designed tasks.
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Vision-guided manipulation, imitation learning, multi-task
learning, geometric computer vision and few-shot learning.
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Cartpole Generalization
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Test generalization through varying the mass and length of the pole
in CartPole.
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Natural RL Environment
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Play common gym tasks with randomly generated backgrounds to test
generalization.
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DMControl Generalization Benchmark
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Generalization benchmark for continuous control tasks from DeepMind Control Suite. Includes hundreds of environments with randomized colors and dynamic video backgrounds of varying difficulty.
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Procgen
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16 simple-to-use procedurally-generated environments which provide
a direct measure of how quickly a reinforcement learning agent
learns generalizable skills.
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The environments run at high speed (thousands of steps per second)
on a single core.
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Animal-AI Testbed
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900 tasks reflecting various cognitive skills of animals.
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Powered by Unity ml-agent.
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Crafter
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Open world survival game that evaluates many agent abilities within one environment.
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Faster and easier than Minecraft but poses some of the same challenges.
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Can be used to evaluate reward-based or unsupervised agents (e.g.
artificial curiosity).
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DeepMind Lab
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Provides a suite of challenging 3D navigation and puzzle-solving
tasks for learning agents.
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gym-maze
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A simple 2D maze environment where an agent (blue dot) finds its
way from the top left corner (blue square) to the goal at the
bottom right corner (red square).
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The objective is to find the
shortest path from the start to the goal.
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gym-minigrid
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Lightweight and fast grid world implementation with various
included tasks.
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Easily modifable and extendable.
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gym-miniworld
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Minimalistic 3D interior simulator as an alternative to VizDoom or
DMLab.
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Easily modifable and extendable.
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Obstacle Tower
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Traverse through procedurally generated floors which get progressively harder.
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Challenging visual inputs.
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AI2THOR
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An Interactive 3D Environment for Visual AI
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Gibson
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3d navigation in indoor scans
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Habitat
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AI Habitat enables training of embodied AI agents (virtual robots)
in a highly photorealistic & efficient 3D simulator, before
transferring the learned skills to reality
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HoME: a Household Multimodal Environment
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A platform for agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context.
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House3D
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House3D is a virtual 3D environment which consists of thousands of
indoor scenes equipped with a diverse set of scene types, layouts
and objects sourced from the SUNCG dataset
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It consists of over 45k indoor 3D scenes, ranging from studios to
two-storied houses with swimming pools and fitness rooms
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All 3D objects are fully annotated with category labels
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Multiple observation modalities
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Fast rendering at thousands of frames per second
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MINOS
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MINOS is a simulator designed to support the development of
multisensory models for goal-directed navigation in complex indoor
environments.
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MINOS leverages large datasets of complex 3D environments and
supports flexible configuration of multimodal sensor suites.
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Nvidia ISAAC simulator
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A virtual robotics laboratory and a high-fidelity 3D world simulator
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VirtualHome
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A 3D environment allowing to simulate and generate videos of activities as sequences of actions and interaction.
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Massive Multi Agent Game Environment
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We consider MMORPGs (Massive
Multiplayer Online Role Playing Games) the best proxy for the real
world among human games: they are complete macrocosms featuring
thousands of agents per persistent world, diverse skilling systems,
global economies, complex emergent social structures, and ad-hoc
high stakes single and team based conflict.
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Multi-agent Particle Environment
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A simple multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics
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OpenAI Multi-Agent Competition Environments
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Contains many continous control, multi-agent tasks.
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OpenAI Multi-Agent Hide and Seek
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A team of seekers and a team of hiders.
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Both teams can use tools to achieve their objective.
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RoboSumo
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Sumo-wrestling between two ants using continuous control.
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Assistive-gym
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6 assistive tasks (ScratchItch, BedBathing, Feeding, Drinking, Dressing, and ArmManipulation).
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4 commercial robots (PR2, Jaco, Baxter, Sawyer).
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2 human states: static or active (takes actions according to a separate control policy).
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Customizable female and male human models. 40 actuated human joints (head, torso, arms, waist, and legs).Realistic human joint limit.
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DeepMind AI Safety Gridworlds
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This is a suite of reinforcement learning environments illustrating
various safety properties of intelligent agents.
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Safety Gym
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Tools for accelerating safe exploration research.
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Autonomous Vehicle Simulator
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Open source simulator for autonomous vehicles built on Unreal Engine
/ Unity, from Microsoft AI & Research
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BARK-ML
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Open source environments and reinforcement learning agents
for autonomous driving and behavior generation.
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CARLA
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CARLA has been developed from the ground up to support development,
training, and validation of autonomous driving systems
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DeepDrive Self Driving Car Simulator
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End-to-end simulation for self-driving cars
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DeepMind StreetLearn
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A C++/Python implementation of the StreetLearn environment based on
images from Street View, as well as a TensorFlow implementation of
goal-driven navigation agents solving the task published in “Learning
to Navigate in Cities Without a Map”, NeurIPS 2018
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DeepGTAV v2
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A plugin for GTAV that transforms it into a vision-based self-driving
car research environment.
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DuckieTown
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Self-driving car simulator for the Duckietown universe.
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Highway-Env
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A collection of environments for autonomous driving and tactical
decision-making tasks
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SVL Simulator
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Simulation software to accelerate safe autonomous vehicle development
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Custom environment to support openai gym interface
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TORCS
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TORCS, The Open Racing Car Simulator is a highly portable multi
platform car racing simulation
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Many tracks, opponents and cars available
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Easy to modify
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Full Body Muscle Simulator
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A basic simulation and control for full-body Musculoskeletal system
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Osim-rl
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Reinforcement learning environments with musculoskeletal models. Task: learning to walk/move/run using musculoskeletal models.
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Roboschool
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Control robots in simulation.
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Can use other physics engines other than MuJoCo.
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Alternative to standard OpenAI Gym mujoco environments.
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Easy to train multiple agents at once.
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Jericho
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A learning environment for man-made Interactive Fiction games.
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TextWorld
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TextWorld is a sandbox learning environment for the training
and evaluation of reinforcement learning (RL) agents on text-based
games.
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Reco Gym
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Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising.
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RecSim
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A Configurable Recommender Systems Simulation Platform from Google.
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The list is not comprehensive, so please let us know if there is any environment that is missing, miscategorized, or needs a different description or image. Please submit an issue or open a pull request.