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• W3138349270 abstract "Abstract Markov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world." @default.
• W3138349270 created "2021-03-29" @default.
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• W3138349270 date "2021-03-20" @default.
• W3138349270 modified "2023-10-16" @default.
• W3138349270 title "Optimal Policies for Quantum Markov Decision Processes" @default.
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• W3138349270 doi "https://doi.org/10.1007/s11633-021-1278-z" @default.
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