Gymnasium/docs/environments.md

# 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](creating-environments.md).

## Included Environments

The code for each environment group is housed in its own subdirectory
[gym/envs](https://github.com/openai/gym/blob/master/gym/envs). The
specification of each task is in
[gym/envs/\_\_init\_\_.py](https://github.com/openai/gym/blob/master/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.

``` python
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:

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

This will install `atari-py`, which automatically compiles the [Arcade
Learning Environment](http://www.arcadelearningenvironment.org/). 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:

``` python
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:

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

### MuJoCo

[MuJoCo](http://www.mujoco.org/) 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](https://github.com/openai/mujoco-py#obtaining-the-binaries-and-license-key)
to set it up. You'll have to also run `pip install -e '.[mujoco]'` if
you didn't do the full install.

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

### Robotics

These environments also use [MuJoCo](http://www.mujoco.org/). You'll have to also run `pip install -e '.[robotics]'` if
you didn't do the full install.

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

You can also find additional details in the accompanying [technical
report](https://arxiv.org/abs/1802.09464) and [blog
post](https://blog.openai.com/ingredients-for-robotics-research/). 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:

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

## OpenAI Environments

### Procgen

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.

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

### 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

### Roboschool (DEPRECATED)

**We recommend using the [PyBullet Robotics Environments](#pybullet-robotics-environments) instead**

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

## 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-env

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](http://duckietown.org/) project (small-scale self-driving car course).

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

### GymFC: A flight control tuning and training framework 

GymFC is a modular framework for synthesizing neuro-flight controllers. The
architecture integrates digital twinning concepts to provide seamless transfer
of trained policies to hardware. The OpenAI environment has been used to
generate policies for the worlds first open source neural network flight
control firmware [Neuroflight](https://github.com/wil3/neuroflight).

Learn more here: https://github.com/wil3/gymfc/

### gym-anytrading: Environments for trading markets

AnyTrading is a collection of OpenAI Gym environments for reinforcement learning-based trading algorithms with a great focus on simplicity, flexibility, and comprehensiveness.

Learn more here: https://github.com/AminHP/gym-anytrading

### GymGo: The Board Game Go

An implementation of the board game Go

Learn more here: https://github.com/aigagror/GymGo 

### gym-electric-motor: Intelligent control of electric drives

An environment for simulating a wide variety of electric drives taking into account different types of electric motors and converters. Control schemes can be continuous, yielding a voltage duty cycle, or discrete, determining converter switching states directly.

Learn more here: https://github.com/upb-lea/gym-electric-motor

### NASGym: gym environment for Neural Architecture Search (NAS)

The environment is fully-compatible with the OpenAI baselines and exposes a NAS environment following the Neural Structure Code of [BlockQNN: Efficient Block-wise Neural Network Architecture Generation](https://arxiv.org/abs/1808.05584). Under this setting, a Neural Network (i.e. the state for the reinforcement learning agent) is modeled as a list of NSCs, an action is the addition of a layer to the network, and the reward is the accuracy after the early-stop training. The datasets considered so far are the CIFAR-10 dataset (available by default) and the meta-dataset (has to be manually downloaded as specified in [this repository](https://github.com/gomerudo/meta-dataset)).

Learn more here: https://github.com/gomerudo/nas-env

### gym-jiminy: training Robots in Jiminy

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.

Learn more here: https://github.com/Wandercraft/jiminy

### highway-env: Tactical Decision-Making for Autonomous Driving

An environment for behavioural planning in autonomous driving, with an emphasis on high-level perception and decision rather than low-level sensing and control. The difficulty of the task lies in understanding the social interactions with other drivers, whose behaviours are uncertain. Several scenes are proposed, such as highway, merge, intersection and roundabout.

Learn more here: https://github.com/eleurent/highway-env

### gym-carla: Gym Wrapper for CARLA Driving Simulator

gym-carla provides a gym wrapper for the [CARLA simulator](http://carla.org/), which is a realistic 3D simulator for autonomous driving research. The environment includes a virtual city with several surrounding vehicles running around. Multiple source of observations are provided for the ego vehicle, such as front-view camera image, lidar point cloud image, and birdeye view semantic mask. Several applications have been developed based on this wrapper, such as deep reinforcement learning for end-to-end autonomous driving.

Learn more here: https://github.com/cjy1992/gym-carla

### openmodelica-microgrid-gym: Intelligent control of microgrids 

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.

Learn more here: https://github.com/upb-lea/openmodelica-microgrid-gym