Deep Reinforcement Learning: Bridging Learning and Control in Intelligent Systems

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dc.contributor.authorIzci, Davut
dc.contributor.authorThanh, Hung Vo
dc.contributor.authorZitar, Raed Abu
dc.contributor.authorAl-Okbi, Nada Khalil
dc.contributor.authorLiu, Zhe
dc.contributor.authorŞahin, Canan Batur
dc.contributor.authorAbualigah, Laith
dc.date.accessioned2026-06-19T06:31:58Z
dc.date.available2026-06-19T06:31:58Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractDeep Reinforcement Learning (DRL) has gained popularity as a new approach in artificial intelligence that successfully integrates representation learning through Deep Learning with decision making in Reinforcement Learning. In this work, we investigate basic concepts of DRL design, structural composition, and scope of usage in intelligent systems. More specifically, several benchmark tasks were assigned to test states and actions of DRL algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO). Empirical evaluations illustrate the potential of DRL in dealing with complex tasks. Sample efficiency, stability and interpretability issues of DRL are also reviewed in the paper. Suggestions for further work are concentrated on algorithm increase of robustness, training time and expense decrease as well as enhancement of implementation efficiency in practical environment. © 2025 Laith Abualigah.
dc.identifier.doi10.1201/9781003516385-17
dc.identifier.endpage141
dc.identifier.isbn978-104045106-9
dc.identifier.isbn978-103283483-2
dc.identifier.scopus2-s2.0-105013297150
dc.identifier.scopusqualityN/A
dc.identifier.startpage134
dc.identifier.urihttps://doi.org/10.1201/9781003516385-17
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4897
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherCRC Press
dc.relation.ispartofA to Z of Deep Learning and AI
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260612
dc.subjectDecision Making
dc.subjectDeep Learning
dc.subjectDeep Reinforcement Learning
dc.subjectEfficiency
dc.subjectOptimization
dc.subjectReinforcement Learning
dc.subjectBasic Concepts
dc.subjectComplex Task
dc.subjectDecisions Makings
dc.subjectEmpirical Evaluations
dc.subjectLearning Designs
dc.subjectNew Approaches
dc.subjectPolicy Optimization
dc.subjectReinforcement Learning Algorithms
dc.subjectReinforcement Learnings
dc.subjectStructural Composition
dc.subjectIntelligent Systems
dc.titleDeep Reinforcement Learning: Bridging Learning and Control in Intelligent Systems
dc.typeBook Chapter

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