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• W2158082107 abstract "Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers (FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinite-horizon policy. This policy is optimized by a new infinite-horizon algorithm to yield deterministic periodic policies, and by a new expectation maximization algorithm to yield stochastic periodic policies. Our method yields better results than earlier planning methods and can compute larger solutions than with regular FSCs." @default.
• W2158082107 created "2016-06-24" @default.
• W2158082107 creator A5017983137 @default.
• W2158082107 creator A5018490229 @default.
• W2158082107 date "2011-12-12" @default.
• W2158082107 modified "2023-09-24" @default.
• W2158082107 title "Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning" @default.
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