The Neural MMO Platform for Massively Multiagent Research

Joseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola

Keywords: Environment, Multiagent, Reinforcement Learning, Simulation, MMO, Platform

TL;DR: Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems.

Abstract: Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first to combine them all. We present Neural MMO as free and open source software with active support, ongoing development, documentation, and additional training, logging, and visualization tools to help users adapt to this new setting. Initial baselines on the platform demonstrate that agents trained in large populations explore more and learn a progression of skills. We raise other more difficult problems such as many-team cooperation as open research questions which Neural MMO is well-suited to answer. Finally, we discuss current limitations of the platform, potential mitigations, and plans for continued development.

URL: neuralmmo.github.io

Contribution Process Agreement: Yes

Dataset Url: neuralmmo.github.io

Dataset Embargo: N/A

License: MIT License

Author Statement: Yes

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/the-neural-mmo-platform-for-massively/code)