Specialization and Exchange in Neural MMO

Joseph Suarez, Phillip Isola

Keywords: Neural MMO, environment, multi-agent, reinforcement learning, specialization, exchange, trade

TL;DR: We contribute a profession and exchange system to Neural MMO (an open-source multi-agent simulation framework) for use in emergent specialization research and present initial findings.

Abstract: We present a simulated profession and exchange system for use in multi-agent intelligence research. Each of the eight implemented jobs produces items required by other professions. As a result, each profession must purchase items that they cannot produce themselves from other professions. These items are then used to produce increasingly high-quality goods for resale on a global market. Better and better goods enter the market as trade among professions creates a feedback loop. We integrate our profession and exchange system with Neural MMO, an existing multi-agent reinforcement learning platform capable of efficiently simulating populations of tens to 1000+ agents. We hope that our work will help support new research on emergent specialization --- the ability to select and commit to a specific long-term strategy that fills a niche left by other learning agents. All of our code, including scripted baseline agents for each profession, will be free, open-source, and actively maintained.