We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems: participants train teams of agents to complete a multi-task objective against opponents not seen during training. The competition combines relatively complex environment design with large numbers of agents in the environment. The top submissions demonstrate strong success on this task using mostly standard reinforcement learning (RL) methods combined with domain-specific engineering. We summarize the competition design and results and suggest that, as an academic community, competitions may be a powerful approach to solving hard problems and establishing a solid benchmark for algorithms. We will open-source our benchmark including the environment wrapper, baselines, a visualization tool, and selected policies for further research.

翻译：我们报告了第二届Neural MMO挑战赛（IJCAI 2022举办）的结果，该赛事收到1600余份参赛作品。本次竞赛聚焦多智能体系统的鲁棒性与泛化能力：参赛者需训练智能体团队，在未见过的对手面前完成多目标任务。竞赛将相对复杂的环境设计与大量智能体共存场景相结合。优胜方案主要采用标准强化学习方法结合领域特定工程技术，在该任务中展现出强劲表现。我们总结了竞赛设计与结果，并提出学术社区可通过竞赛形式攻克难题并建立稳健算法基准的论断。我们将开源基准系统，包括环境封装器、基线模型、可视化工具及精选策略以供后续研究。