AAAI Publications, Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence

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Markov Games of Incomplete Information for Multi-Agent Reinforcement Learning
Liam MacDermed, Charles Isbell, Lora Weiss

Last modified: 2011-08-24

Abstract


Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully observable stochastic game. MGIIs represents the most general tractable model for multi-agent reinforcement learning to date.

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