Special issue alert!
Intelligent decision-making systems are becoming ubiquitous as both software and hardware are becoming more sophisticated. However, due to the large amount of data generated by modern systems, traditional decision-making approaches fail to function accordingly since the state-spaces become too vast to allow for analytical analysis. Reinforcement learning involves autonomous agents that learn optimal policies that allow them to navigate their environment by maximizing cumulative rewards.
This Special Issue aims to gather cutting-edge research papers in applied, scalable reinforcement learning for smart, autonomous decision-making of intelligent systems such as fleets of autonomous vehicles, energy systems, network optimization, or other related areas.
We welcome original research articles, review and survey papers, and innovative case studies that focus on reinforcement learning algorithms, intelligent systems, autonomous decision-making, digital twins, and smart manufacturing. Papers in other domains such as finance or medicine will also be considered.
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Erotokritos Skordilis
Lecturer
University of Miami, Herbert Business School, Department of Business Technology
Miami FL
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