The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent
  Competition with Specialization and Trade
Enhong Liu Joseph Suarez Chenhui You Bo Wu Bingcheng Chen Jun Hu Jiaxin Chen Xiaolong Zhu Clare Zhu Julian Togelius Sharada Mohanty Weijun Hong Rui Du Yibing Zhang Qinwen Wang Xinhang Li Zheng Yuan Xiang Li Yuejia Huang Kun Zhang Hanhui Yang Shiqi Tang Phillip Isola
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.