Submission Deadline: 31 December 2026 View: 85 Submit to Special Issue
Prof. Batyrkhan Omarov
Email: batyahan@gmail.com
Affiliation: Department of Mathematical and Computer Modeling, International Information Technology University, Almaty, Kazakhstan
Research Interests: reinforcement learning algorithms, deep reinforcement learning, model-free and model-based RL, policy optimization methods, continuous control and decision-making, safe and constrained reinforcement learning, multi-agent reinforcement learning, reinforcement learning for cyber physical systems, RL for robotics and autonomous systems, learning-based control, real-time reinforcement learning, explainable and trustworthy RL
Prof. Azizah Suliman
Email: azizah.suliman@amu.edu.my
Affiliation: Faculty of Science and Technology, Asia Metropolitan University, Subang Jaya Campus, Malaysia
Research Interests: intelligent manufacturing systems, reinforcement learning optimization, adaptive and predictive control, industrial robotics, collaborative robots, model-free and model-based reinforcement learning, edge intelligence, autonomous decision-making systems, safe and robust control, real-time embedded AI, industrial cyber security, data-driven control systems
Prof. Aigerim Altayeva
Email: aigerim.altayeva@kaznu.edu.kz
Affiliation: Al-Farabi Kazakh National University, Almaty, Kazakhstan
Research Interests: reinforcement learning, cyber physical systems, smart manufacturing, industrial automation, robotics and autonomous systems, intelligent control, human-in-the-loop learning, multi-agent reinforcement learning, computer vision and sensor fusion, digital twins, internet of things, edge and cloud computing, digital health systems, medical cyber physical systems, safe and explainable AI, real-time optimization, industrial AI, intelligent healthcare systems
Cyber Physical Systems are increasingly becoming intelligent, autonomous, and adaptive through the integration of reinforcement learning techniques that enable real-time decision-making in complex physical environments. The convergence of reinforcement learning and CPS is driving major advances across smart manufacturing, robotics, and digital health applications.
The aim of this Special Issue is to present recent theoretical advances, methodological developments, and real-world applications of reinforcement learning in cyber physical systems. It focuses on how reinforcement learning enables autonomous control, optimization, and adaptation in tightly coupled cyber–physical environments across domains such as smart manufacturing, robotics, autonomous systems, and digital health. The Special Issue welcomes interdisciplinary contributions that address algorithm design, system integration, safety, robustness, and deployment challenges, with an emphasis on scalable, data-driven, and human-centered CPS solutions validated through simulations, digital twins, or real-world implementations.
Suggested themes are listed below:
- Reinforcement learning architectures and algorithms for cyber physical systems
- RL-based control, optimization, and decision-making in smart manufacturing
- Reinforcement learning for robotics, autonomous systems, and intelligent manipulation
- Safe, robust, and explainable reinforcement learning for safety-critical CPS
- Multi-agent and distributed reinforcement learning in cooperative CPS
- Digital twins and simulation-driven reinforcement learning for CPS design and validation
- Reinforcement learning applications in digital health, medical devices, and intelligent healthcare systems
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