2020/01/31 by Joseph Suárez, Joseph Suarez, Suarez, Joseph +6 · 1 voice
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multiagent Systems (cs.MA) #Neural Networks and Applications #Reinforcement Learning in Robotics #cs.AI #cs.LG #cs.MA #stat.ML
paper · pdf · doi:10.48550/arxiv.2001.12004
openalex publication_date 2020/01/31 · arxiv published 2020/01/31 · arxiv created 2020/04/17 · arxiv updated 2020/04/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.