Simulated games have become a dominant platform for multiagent intelligence research in recent years. Previous works have succeeded 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 a massively multiagent game environment inspired by MMOs and demonstrate that simple policy gradient methods produce interesting emergent exploration and specialization behaviors.
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