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The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade

arXiv 2311.03707 ↗First posted 7 Nov 2023cs.AI cs.LG cs.MAcited by 0

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Abstract (arXiv)

In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1,600 submissions. Like the previous IJCAI-2022 Neural MMO Challenge, it involved agents from 16 populations surviving in procedurally generated worlds by collecting resources and defeating opponents. This year's competition runs on the latest v1.6 Neural MMO, which introduces new equipment, combat, trading, and a better scoring system. These elements combine to pose additional robustness and generalization challenges not present in previous competitions. This paper summarizes the design and results of the challenge, explores the potential of this environment as a benchmark for learning methods, and presents some practical reinforcement learning training approaches for complex tasks with sparse rewards. Additionally, we have open-sourced our baselines, including environment wrappers, benchmarks, and visualization tools for future research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 7 Nov 2023.

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References · 6

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  3. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  4. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  5. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  6. 2018Pommerman: A Multi-Agent Playgroundcited by 30

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