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The Third Neural MMO Challenge: Learning to Specialize in Massively Multiagent Open Worlds
Joseph Suarez · Hanmo Chen · Arbin Chen · Bo Wu · Xiaolong Zhu · enhong liu · JUN HU · Chenghui Yu · Phillip Isola

Thu Dec 08 01:00 PM -- 04:00 PM (PST) @ Virtual

Neural MMO is an open-source environment for agent-based intelligence research featuring large maps with large populations, long time horizons, and open-ended multi-task objectives. We propose a benchmark on this platform wherein participants train and submit agents to accomplish loosely specified goals -- both as individuals and as part of a team. The submitted agents are evaluated against thousands of other such user submitted agents. Participants get started with a publicly available code base for Neural MMO, scripted and learned baseline models, and training/evaluation/visualization packages. Our objective is to foster the design and implementation of algorithms and methods for adapting modern agent-based learning methods (particularly reinforcement learning) to a more general setting not limited to few agents, narrowly defined tasks, or short time horizons. Neural MMO provides a convenient setting for exploring these ideas without the computational inefficiency typically associated with larger environments.

Author Information

Joseph Suarez (Massachusetts Institute of Technology)
Hanmo Chen
Arbin Chen
Bo Wu (parametrix.ai)
Xiaolong Zhu (University of Hong Kong)
enhong liu
JUN HU (Parametrix.AI)
Chenghui Yu (parametrix.ai)
Phillip Isola (Massachusetts Institute of Technology)

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