Fuzzy Reinforcement Learning Control for Decentralized Partially Observable Markov Decision Processes

Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) offer a powerful platform for optimizing sequential decision making in partially observable stochastic environments. However, finding optimal solutions for Dec-POMDPs is known to be intractable, necessitating approximate/suboptimal approaches. To address this problem, this work proposes a novel fuzzy reinforcement learning (RL) based game theoretic controller for Dec-POMDPs. The proposed controller implements fuzzy RL on Dec-POMDPs, which are modeled as a sequence of Bayesian games (BG). The main contributions of the work are the introduction of a game based RL paradigm in a Dec-POMDP settings, and the use of fuzzy inference systems to effectively generalize the underlying belief space. We apply the proposed technique on two benchmark problems and compare results against state-of-the-art Dec-POMDP control approach. The results validate the feasibility and effectiveness of using game theoretic RL based fuzzy control for addressing intractability of Dec-POMDPs, thus opening up a new research direction.