Deep Reinforcement Learning: Bridging Learning and Control in Intelligent Systems
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
CRC Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Deep 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.
Açıklama
Anahtar Kelimeler
Decision Making, Deep Learning, Deep Reinforcement Learning, Efficiency, Optimization, Reinforcement Learning, Basic Concepts, Complex Task, Decisions Makings, Empirical Evaluations, Learning Designs, New Approaches, Policy Optimization, Reinforcement Learning Algorithms, Reinforcement Learnings, Structural Composition, Intelligent Systems
Kaynak
A to Z of Deep Learning and AI
WoS Q Değeri
Scopus Q Değeri
N/A
