Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …

Entropy regularization is commonly used to improve policy optimization in reinforcement
learning. It is believed to help with exploration by encouraging the selection of more …

This article delves into the challenging supply chain management domain, explicitly
addressing the intricate issue of perishable inventory allocation within a two-echelon supply …

Wireless body area networks (WBANs) are vulnerable to active eavesdropping that
simultaneously perform sniffing and jamming to raise the sensor transmit power, and thus …

Mobile health (mHealth) intervention systems can employ adaptive strategies to interact with
users. Instead of designing such complex strategies manually, reinforcement learning (RL) …

As the extension of cyber–physical systems (CPSs), cyber–physical–social systems
(CPSSs) seamlessly integrate cyber space, physical space, and social space. CPSS provide …

This study presents a benchmark for evaluating action-constrained reinforcement learning
(RL) algorithms. In action-constrained RL, each action taken by the learning system must …

We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art
performance but are also simple and minimalistic. For standard continuous control …

Localizing keypoints of an object is a basic visual problem. However, supervised learning of
a keypoint localization network often requires a large amount of data, which is expensive …

Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-
world applications. Nonetheless, LLMs are often criticized for their tendency to produce …