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Environments

This is a list of Gym environments, including those packaged with Gym, official OpenAI environments, and third party environment.

For information on creating your own environment, see Creating your own Environment.

Included Environments

The code for each environment group is housed in its own subdirectory gym/envs. The specification of each task is in gym/envs/__init__.py. It's worth browsing through both.

Algorithmic

These are a variety of algorithmic tasks, such as learning to copy a sequence.

import gym
env = gym.make('Copy-v0')
env.reset()
env.render()

Atari

The Atari environments are a variety of Atari video games. If you didn't do the full install, you can install dependencies via pip install -e '.[atari]' (you'll need cmake installed) and then get started as follows:

import gym
env = gym.make('SpaceInvaders-v0')
env.reset()
env.render()

This will install atari-py, which automatically compiles the Arcade Learning Environment. This can take quite a while (a few minutes on a decent laptop), so just be prepared.

Box2d

Box2d is a 2D physics engine. You can install it via pip install -e '.[box2d]' and then get started as follows:

import gym
env = gym.make('LunarLander-v2')
env.reset()
env.render()

Classic control

These are a variety of classic control tasks, which would appear in a typical reinforcement learning textbook. If you didn't do the full install, you will need to run pip install -e '.[classic_control]' to enable rendering. You can get started with them via:

import gym
env = gym.make('CartPole-v0')
env.reset()
env.render()

MuJoCo

MuJoCo is a physics engine which can do very detailed efficient simulations with contacts. It's not open-source, so you'll have to follow the instructions in mujoco-py to set it up. You'll have to also run pip install -e '.[mujoco]' if you didn't do the full install.

import gym
env = gym.make('Humanoid-v2')
env.reset()
env.render()

Robotics

MuJoCo is a physics engine which can do very detailed efficient simulations with contacts and we use it for all robotics environments. It's not open-source, so you'll have to follow the instructions in mujoco-py to set it up. You'll have to also run pip install -e '.[robotics]' if you didn't do the full install.

import gym
env = gym.make('HandManipulateBlock-v0')
env.reset()
env.render()

You can also find additional details in the accompanying technical report and blog post. If you use these environments, you can cite them as follows:

@misc{1802.09464,
  Author = {Matthias Plappert and Marcin Andrychowicz and Alex Ray and Bob McGrew and Bowen Baker and Glenn Powell and Jonas Schneider and Josh Tobin and Maciek Chociej and Peter Welinder and Vikash Kumar and Wojciech Zaremba},
  Title = {Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research},
  Year = {2018},
  Eprint = {arXiv:1802.09464},
}

Toy text

Toy environments which are text-based. There's no extra dependency to install, so to get started, you can just do:

import gym
env = gym.make('FrozenLake-v0')
env.reset()
env.render()

OpenAI Environments

Roboschool

3D physics environments like Mujoco environments but uses the Bullet physics engine and does not require a commercial license.

Learn more here: https://github.com/openai/roboschool

Gym-Retro

Gym Retro lets you turn classic video games into Gym environments for reinforcement learning and comes with integrations for ~1000 games. It uses various emulators that support the Libretro API, making it fairly easy to add new emulators.

Learn more here: https://github.com/openai/retro

Third Party Environments

The gym comes prepackaged with many many environments. It's this common API around many environments that makes Gym so great. Here we will list additional environments that do not come prepacked with the gym. Submit another to this list via a pull-request.

Pybullet Robotics Environments

3D physics environments like the Mujoco environments but uses the Bullet physics engine and does not require a commercial license. Works on Mac/Linux/Windows.

Learn more here: https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit#heading=h.wz5to0x8kqmr

Obstacle Tower

3D procedurally generated tower where you have to climb to the highest level possible

Learn more here: https://github.com/Unity-Technologies/obstacle-tower-challenge

Platforms: Windows, Mac, Linux

PGE: Parallel Game Engine

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.

Learn more here: https://github.com/222464/PGE

gym-inventory: Inventory Control Environments

gym-inventory is a single agent domain featuring discrete state and action spaces that an AI agent might encounter in inventory control problems.

Learn more here: https://github.com/paulhendricks/gym-inventory

gym-gazebo: training Robots in Gazebo

gym-gazebo presents an extension of the initial OpenAI gym for robotics using ROS and Gazebo, an advanced 3D modeling and rendering tool.

Learn more here: https://github.com/erlerobot/gym-gazebo/

gym-maze: 2D maze environment

A simple 2D maze environment where an agent finds its way from the start position to the goal.

Learn more here: https://github.com/tuzzer/gym-maze/

osim-rl: 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.

Learn more here: https://github.com/stanfordnmbl/osim-rl

gym-minigrid: Minimalistic Gridworld Environment

A minimalistic gridworld environment. Seeks to minimize software dependencies, be easy to extend and deliver good performance for faster training.

Learn more here: https://github.com/maximecb/gym-minigrid

gym-miniworld: Minimalistic 3D Interior Environment Simulator

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.

Learn more here: https://github.com/maximecb/gym-miniworld

gym-sokoban: 2D Transportation Puzzles

The environment consists of transportation puzzles in which the player's goal is to push all boxes on 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 over fitting to predefined levels.

Learn more here: https://github.com/mpSchrader/gym-sokoban

gym-duckietown: Lane-Following Simulator for Duckietown

A lane-following simulator built for the Duckietown project (small-scale self-driving car course).

Learn more here: https://github.com/duckietown/gym-duckietown