Intelligent Trading Agents for Massively Multi-player Game Economies

John Reeder, Gita Sukthankar, Michael Georgiopoulos, Georgios Anagnostopoulos

As massively multi-player gaming environments become more detailed, developing agents to populate these virtual worlds as capable non-player characters poses an increasingly complex problem. Human players in many games must achieve their objectives through financial skills such as trading and supply chain management as well as through combat and diplomacy. In this paper, we examine the problem of creating intelligent trading agents for virtual markets. Using historical data from EVE Online, a science-fiction based MMORPG, we evaluate several strategies for buying, selling, and supply chain management. We demonstrate that using reinforcement learning to determine policies based on the market microstructure gives trading agents a competitive advantage in amassing wealth. Imbuing agents with the ability to adapt their trading policies can make them more resistant to exploitation by other traders and capable of participating in virtual economies on an equal footing with humans.

Subjects: 12.1 Reinforcement Learning; 1.8 Game Playing

Submitted: Aug 12, 2008


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