Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) offer a porful platform for optimizing sequential decision making in partially observable stochastic environments. Hover, 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.  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.
['Rajneesh Sharma', 'Matthijs T. J. Spaan']
I am writing a research paper on a idea Fuzzy reinforcement learning control for decentralized partially observable Markov decision processes
[{"from": "human", "value": "I am writing a research paper on a idea Fuzzy reinforcement learning control for decentralized partially observable Markov decision processes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) offer a porful platform for optimizing sequential decision making in partially observable stochastic environments. Hover, 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.  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."}]