Minimalistic 3D interior environment simulator for reinforcement learning & robotics research.
Project description
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
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for miniworld-2.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d2d0a8722b4495f9de75846651668751ba17e5f15169d93ee78fd14dea66c38d |
|
MD5 | 767c421a3fc78e2454b9275c00f7e333 |
|
BLAKE2b-256 | 5f442b5aab45aa377e3dfdbfa37ac334a1aefe00fa0202a6fae0cc1d5de10854 |