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Progress in multiagent intelligence research is fundamentally limited by thenumber and quality of environments available for study. In recent years,simulated games have become a dominant research platform within reinforcementlearning, in part due to their accessibility and interpretability. Previousworks have targeted and demonstrated success on arcade, first person shooter(FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games.Our work considers massively multiplayer online role-playing games (MMORPGs orMMOs), which capture several complexities of real-world learning that are notwell modeled by any other game genre. We present Neural MMO, a massivelymultiagent game environment inspired by MMOs and discuss our progress on twomore general challenges in multiagent systems engineering for AI research:distributed infrastructure and game IO. We further demonstrate that standardpolicy gradient methods and simple baseline models can learn interestingemergent exploration and specialization behaviors in this setting.

Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks