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Titlebook: Optimization and Games for Controllable Markov Chains; Numerical Methods wi Julio B. Clempner,Alexander Poznyak Book 2024 The Editor(s) (if

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書(shū)目名稱(chēng)Optimization and Games for Controllable Markov Chains
副標(biāo)題Numerical Methods wi
編輯Julio B. Clempner,Alexander Poznyak
視頻videohttp://file.papertrans.cn/704/703212/703212.mp4
概述Considers a class of ergodic finite controllable Markov‘s chains.Develops the original discrete optimization problems (or game models) in the space of randomized formulations.Focuses on how Markov cha
叢書(shū)名稱(chēng)Studies in Systems, Decision and Control
圖書(shū)封面Titlebook: Optimization and Games for Controllable Markov Chains; Numerical Methods wi Julio B. Clempner,Alexander Poznyak Book 2024 The Editor(s) (if
描述.This book considers a class of ergodic finite controllable Markov‘s chains. The main idea behind the method, described in this book, is to develop the original discrete optimization problems (or game models) in the space of randomized formulations, where the variables stand in for the distributions (mixed strategies or preferences) of the original discrete (pure) strategies in the use. The following suppositions are made: a finite state space, a limited action space, continuity of the probabilities and rewards associated with the actions, and a necessity for accessibility. These hypotheses lead to the existence of an optimal policy. The best course of action is always stationary. It is either simple (i.e., nonrandomized stationary) or composed of two nonrandomized policies, which is equivalent to randomly selecting one of two simple policies throughout each epoch by tossing a biased coin. As a bonus, the optimization procedure just has to repeatedly solve the time-average dynamic programming equation, making it theoretically feasible to choose the optimum course of action under the global restriction. In the ergodic cases the state distributions, generated by the corresponding tra
出版日期Book 2024
關(guān)鍵詞Finance and Engineering; Controllable Markov Chains; Markov Systems; Optimization Problems; Game Theory;
版次1
doihttps://doi.org/10.1007/978-3-031-43575-1
isbn_softcover978-3-031-43577-5
isbn_ebook978-3-031-43575-1Series ISSN 2198-4182 Series E-ISSN 2198-4190
issn_series 2198-4182
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Continuous-Time Markov Chains,erature that reduces a linear functional of interest. The second is a chemical reaction network that involves the proton transfer, hydration, and tautomeric reaction of anthocyanin pigments, in this case we found an optimal strategy over a set of values of pH that minimizes the corresponding linear functional.
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Partially Observable Markov Chains,o disclose only partial knowledge about the problem’s structure and parameters. Since some state variables are difficult to track and measure correctly, it may be more beneficial to base judgments on less accurate information. This chapter focuses on the design of an observer for a class of partiall
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