Metadata-Version: 2.1
Name: miniworld
Version: 1.0.0
Author: Farama Foundation
Author-email: jkterry@farama.org
Keywords: environment,agent,rl,gym,robotics,3d
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7, <3.11
Description-Content-Type: text/markdown
License-File: LICENSE

# MiniWorld (gym-miniworld)

[![Build Status](https://travis-ci.org/maximecb/gym-miniworld.svg?branch=master)](https://travis-ci.org/maximecb/gym-miniworld)

Contents:
- [Introduction](#introduction)
- [Installation](#installation)
- [Usage](#usage)
- [Environments](docs/environments.md)
- [Design and Customization](docs/design.md)
- [Troubleshooting](docs/troubleshooting.md)

## Introduction

MiniWorld is a minimalistic 3D interior environment simulator for reinforcement
learning &amp; 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 a simpler alternative to VizDoom or DMLab. It is written
100% in Python and designed to be easily modified or extended by students.

<p align="center">
<img src="images/maze_top_view.jpg" width=260></img>
<img src="images/sidewalk_0.jpg" width=260></img>
<img src="images/collecthealth_0.jpg" width=260></img>
</p>

Features:
- Few dependencies, less likely to break, easy to install
- Easy to create your own levels, or modify existing ones
- Good performance, high frame rate, support for multiple processes
- Lightweight, small download, low memory requirements
- Provided under a permissive MIT license
- Comes with a variety of free 3D models and textures
- Fully observable [top-down/overhead view](images/maze_top_view.jpg) available
- [Domain randomization](https://blog.openai.com/generalizing-from-simulation/) support, for sim-to-real transfer
- Ability to [display alphanumeric strings](images/textframe.jpg) on walls
- Ability to produce depth maps matching camera images (RGB-D)

Limitations:
- Graphics are basic, nowhere near photorealism
- Physics are very basic, not sufficient for robot arms or manipulation

Please use this bibtex if you want to cite this repository in your publications:

```
@misc{gym_miniworld,
  author = {Chevalier-Boisvert, Maxime},
  title = {MiniWorld: Minimalistic 3D Environment for RL & Robotics Research},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/maximecb/gym-miniworld}},
}
```

List of publications & submissions using MiniWorld (please open a pull request to add missing entries):
- [Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices](https://arxiv.org/abs/2008.02790) (Stanford University, ICML 2021)
- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://openreview.net/forum?id=MtEE0CktZht) (Texas A&M University, Kuai Inc., ICLR 2021)
- [DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs](https://arxiv.org/abs/2010.08891) (NeurIPS Offline RL Workshop, Oct 2020)
- [Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning](https://arxiv.org/abs/2007.05196) (University of Antwerp, Jul 2020, ICML 2020 LaReL Workshop)
- [Temporal Abstraction with Interest Functions](https://arxiv.org/abs/2001.00271) (Mila, Feb 2020, AAAI 2020)
- [Addressing Sample Complexity in Visual Tasks Using Hindsight Experience Replay and Hallucinatory GANs](https://openreview.net/forum?id=H1xSXdV0i4) (Offworld Inc, Georgia Tech, UC Berkeley, ICML 2019 Workshop RL4RealLife)
- [Avoidance Learning Using Observational Reinforcement Learning](https://arxiv.org/abs/1909.11228) (Mila, McGill, Sept 2019)
- [Visual Hindsight Experience Replay](https://arxiv.org/pdf/1901.11529.pdf) (Georgia Tech, UC Berkeley, Jan 2019)

This simulator was created as part of work done at [Mila](https://mila.quebec/).

