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Markov games can be used as a platform to deal with exogenous disturbances and parametric variations. In this work an attempt has been made to achieve a superior performance with fuzzy Markov game based control by hybridizing two game theory based approaches of ‘fictitious play’ and ‘minimax’. The work attempts a ‘safe yet consistent’ Markov game controller which advocates a minimax policy during...
This paper proposes a Bayesian-game-based fuzzy reinforcement learning (RL) controller for decentralized partially observable Markov decision processes (Dec-POMDPs). Dec-POMDPs have recently emerged as a powerful platform for optimizing multiagent sequential decision making in partially observable stochastic environments. However, finding exact optimal solutions to a Dec-POMDP is provably intractable...
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)...
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...
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