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This paper develops a novel fuzzy reinforcement learning (RL) based controller for multiagent partially observable Markov decision processes (POMDPs) modeled as a sequence of Bayesian games. Multiagent POMDPs have emerged as a powerful framework for modeling and optimizing multiagent sequential decision making problems under uncertainty, but finding optimal policies is computationally very challenging. Our aim here is twin fold, (i) introduction of a learning paradigm in infinite horizon multiagent POMDPs and (ii) scaling up multiagent POMDP solution approaches by introduction of fuzzy inference systems (FIS) based generalization. We introduce what may be called fuzzy multiagent POMDPs to overcome space and time complexity issues involved in finding optimal policies for multiagent POMDPs. The proposed FIS based RL controller approximates optimal policies for multiagent POMDPs modeled as a sequence of Bayesian games. We empirically evaluate the proposed fuzzy multiagent POMDP controller on the standard benchmark multiagent tiger problem and compare its performance against other state-of-the-art multiagent POMDP solution approaches. Results showcase the effectiveness of the proposed approach and validate the feasibility of employing Bayesian game based RL (in conjunction with FIS approximation) for addressing the intractability of multiagent POMDPs.
Date of Conference: 18-23 July 2010