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Computer Science > Artificial Intelligence

arXiv:2311.03736 (cs)
[Submitted on 7 Nov 2023]

Title:Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning

Authors:Joseph Suárez, Phillip Isola, Kyoung Whan Choe, David Bloomin, Hao Xiang Li, Nikhil Pinnaparaju, Nishaanth Kanna, Daniel Scott, Ryan Sullivan, Rose S. Shuman, Lucas de Alcântara, Herbie Bradley, Louis Castricato, Kirsty You, Yuhao Jiang, Qimai Li, Jiaxin Chen, Xiaolong Zhu
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Abstract:Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define a broad range of objectives and reward signals. We challenge researchers to train agents capable of generalizing to tasks, maps, and opponents never seen during training. Neural MMO features procedurally generated maps with 128 agents in the standard setting and support for up to. Version 2.0 is a complete rewrite of its predecessor with three-fold improved performance and compatibility with CleanRL. We release the platform as free and open-source software with comprehensive documentation available at this http URL and an active community Discord. To spark initial research on this new platform, we are concurrently running a competition at NeurIPS 2023.
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2311.03736 [cs.AI]
  (or arXiv:2311.03736v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2311.03736

Submission history

From: Joseph Suarez [view email]
[v1] Tue, 7 Nov 2023 05:36:39 UTC (2,785 KB)
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