Uczenie ze wzmocnieniem w robotyce i systemach autonomicznych

Uczenie ze wzmocnieniem w robotyce i systemach autonomicznych

Dr. G.M.KarpuraDheepan / Dr. N.S.Usha / Dr. N.Srinivasan

92,35 €
IVA incluido
Disponible
Editorial:
KS OmniScriptum Publishing
Año de edición:
2025
ISBN:
9786209082801
92,35 €
IVA incluido
Disponible

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Sobre IBD

Uczenie ze wzmocnieniem (RL) stało się przełomowym podejściem w dziedzinie systemów autonomicznych, umożliwiając inteligentne podejmowanie decyzji i kontrolę w robotyce, autonomicznych samochodach, opiece zdrowotnej, automatyce przemysłowej i inteligentnej infrastrukturze. W trakcie tej dyskusji zbadaliśmy podstawowe koncepcje, metodologie, wyzwania i rzeczywiste zastosowania RL w systemach autonomicznych, podkreślając zarówno jej potencjał, jak i ograniczenia. Zastosowanie RL w robotyce i systemach autonomicznych opiera się na procesach decyzyjnych Markowa (MDP), które zapewniają ustrukturyzowane ramy dla sekwencyjnego podejmowaniadecyzji . Rozwój metod opartych na wartościach, takich jak Deep Q Networks (DQN), oraz podejść opartych na polityce, takich jak metody Policy Gradient i Actor Critic, umożliwił robotom i autonomicznym agentom uczenie się złożonych zachowań metodą prób i błędów. Co więcej, bezmodelowe i oparte na modelach techniki RL oferują różne kompromisy pod względem wydajności próbkowania i zdolności adaptacyjnych, torując drogę dla bardziej wszechstronnych i praktycznych kontrolerów opartych na uczeniu się.

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