Digital Twins Paradigm: A Systematic Review from the Reinforcement Learning Perspective

Shahmir Khan Mohammed, Shakti Singh, Rabeb Mizouni, Hadi Otrok, Ernesto Damiani

Abstract: The Digital Twins (DT) paradigm has emerged as a powerful tool for simulating and analyzing complex systems in various domains. A DT is a virtual representation of a real-world object(s) whose goal is to accurately emulate real systems, optimize processes, minimize synchronization delays, cut down on overhead, and automate decision-making. DT technology is moving at a faster than expected pace with advances in Artificial Intelligence (AI), Internet of Things (IoT), Distributed Computing, and 5/6G. Being a highly beneficial technology, DT still faces issues of - (1) limited adaptability, (2) incomplete model representation, (3) suboptimal decision making, (4) limited generalization, and (5) scalability and computational efficiency. Reinforcement Learning (RL) offers unsupervised decision-making and intelligence, which can be immensely beneficial in addressing the current challenges faced by DT. This study offers a thorough analysis of the DT paradigm from the standpoint of RL. The survey compares and contrasts existing reinforcement learning-based Digital Twin frameworks, assessing their advantages and disadvantages. Moreover, discussions of approaches highlighting the tradeoffs between simulation fidelity and computing complexity is also studied. Additionally, a thorough understanding of the Digital Twins paradigm from a reinforcement learning perspective, is presented as a helpful resource for academics and industry professionals in the field. Finally, future research directions in this developing field at the nexus of digital modeling, simulation, and artificial intelligence is discussed.

External IDs:dblp:journals/csur/MohammedSMOD26