miniworld 2.0.0

Minimalistic 3D interior environment simulator for reinforcement learning & robotics research.

Miniworld (formerly gym-miniworld) is currently under development to be made compliant with the standards of the Farama Foundation (https://farama.org/project_standards), and when complete this will be maintained long term.

Contents:

• Introduction
• Installation
• Usage
• Environments
• Design and Customization
• Troubleshooting

Introduction

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 a simpler alternative to VizDoom or DMLab. It is written 100% in Python and designed to be easily modified or extended by students.

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 available
• Domain randomization support, for sim-to-real transfer
• Ability to display alphanumeric strings 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 (Stanford University, ICML 2021)
• Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments (Texas A&M University, Kuai Inc., ICLR 2021)
• DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs (NeurIPS Offline RL Workshop, Oct 2020)
• Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning (University of Antwerp, Jul 2020, ICML 2020 LaReL Workshop)
• Temporal Abstraction with Interest Functions (Mila, Feb 2020, AAAI 2020)
• Addressing Sample Complexity in Visual Tasks Using Hindsight Experience Replay and Hallucinatory GANs (Offworld Inc, Georgia Tech, UC Berkeley, ICML 2019 Workshop RL4RealLife)
• Avoidance Learning Using Observational Reinforcement Learning (Mila, McGill, Sept 2019)
• Visual Hindsight Experience Replay (Georgia Tech, UC Berkeley, Jan 2019)

This simulator was created as part of work done at Mila.