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We present the results of the second Neural MMO challenge, hosted at IJCAI2022, which received 1600+ submissions. This competition targets robustness andgeneralization in multi-agent systems: participants train teams of agents tocomplete a multi-task objective against opponents not seen during training. Thecompetition combines relatively complex environment design with large numbersof agents in the environment. The top submissions demonstrate strong success onthis task using mostly standard reinforcement learning (RL) methods combinedwith domain-specific engineering. We summarize the competition design andresults and suggest that, as an academic community, competitions may be apowerful approach to solving hard problems and establishing a solid benchmarkfor algorithms. We will open-source our benchmark including the environmentwrapper, baselines, a visualization tool, and selected policies for furtherresearch.

Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO