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

• URL: http://arxiv.org/abs/2001.12004v2
• Date: Fri, 17 Apr 2020 01:58:32 GMT
• Title: Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
• Authors: Joseph Suarez, Yilun Du, Igor Mordatch, Phillip Isola
• Abstract summary: We present Neural MMO, a massively multiagent game environment inspired by MMOs. We discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO.
• Score: 48.5733173329785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works 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 or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO. We further demonstrate that standard policy gradient methods and simple baseline models can learn interesting emergent exploration and specialization behaviors in this setting.

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