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Learning automata and their applications to intelligent systems /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Learning automata and their applications to intelligent systems // JunQi Zhang, MengChu Zhou.
作者:	Zhang, JunQi
其他作者:	Zhou, MengChu.
出版者:	Hoboken, N.J. :Wiley ; : c2024.,
面頁冊數:	xvii, 251 p. :ill. ; : 24 cm.;
標題:	Machine theory. -
ISBN:	9781394188499 (cloth) :

Learning automata and their applications to intelligent systems /
Zhang, JunQi(Professor)

Learning automata and their applications to intelligent systems /JunQi Zhang, MengChu Zhou. - Hoboken, N.J. :Wiley ;c2024. - xvii, 251 p. :ill. ;24 cm.

Includes bibliographical references and index.

"A learning automaton represents an important and powerful tool in the area of reinforcement learning and aims at learning the optimal one that maximizes the probability of being rewarded out of a set of allowable systems, actions, alternatives, candidates, or designs by the interaction with a random environment. During a cycle, an automaton chooses an action and then receives a stochastic response that can be either a reward or penalty from the environment. The action probability vector of choosing the next action is then updated by employing this response. The ability of learning how to choose the optimal action endows learning automata with high adaptability to the environment, thus saving great expense and time to find the optimal one in various difficult stochastic environments."--

ISBN: 9781394188499 (cloth) :NT4723

LCCN: 2023040653Subjects--Topical Terms:

527775
Machine theory.

LC Class. No.: QA267 / .Z43 2024

Dewey Class. No.: 629.8/92631

Learning automata and their applications to intelligent systems /
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245 1 0 $a Learning automata and their applications to intelligent systems / $c JunQi Zhang, MengChu Zhou.
260 # $a Hoboken, N.J. : $b Wiley ; $a Piscataway, NJ : $b IEEE Press, $c c2024.
300 $a xvii, 251 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and index.
520 # $a "A learning automaton represents an important and powerful tool in the area of reinforcement learning and aims at learning the optimal one that maximizes the probability of being rewarded out of a set of allowable systems, actions, alternatives, candidates, or designs by the interaction with a random environment. During a cycle, an automaton chooses an action and then receives a stochastic response that can be either a reward or penalty from the environment. The action probability vector of choosing the next action is then updated by employing this response. The ability of learning how to choose the optimal action endows learning automata with high adaptability to the environment, thus saving great expense and time to find the optimal one in various difficult stochastic environments."-- $c Provided by publisher.
650 # 0 $a Machine theory. $3 527775
700 1 # $a Zhou, MengChu. $3 745384