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.

翻译：本文展示了NeurIPS-2022神经MMO挑战赛的结果，该赛事吸引了500名参赛者，共收到超过1600份提交。与之前的IJCAI-2022神经MMO挑战赛类似，本赛事要求来自16个种群的智能体在程序生成的虚拟世界中通过采集资源和击败对手来生存。本届挑战赛基于最新的NeurMMO v1.6版本运行，新增了装备系统、战斗机制、交易功能以及更完善的评分体系。这些要素共同构成了本届竞赛特有的鲁棒性与泛化性挑战，而此前赛事并未涉及此类问题。本文总结了挑战赛的设计方案与最终成果，探讨了该环境作为学习方法基准的潜力，并针对具有稀疏奖励的复杂任务提出了若干实用的强化学习训练方法。此外，我们已开源基线系统，包括环境封装接口、基准测试工具及可视化组件，以供后续研究使用。