Employees' behavior and corporate social responsibility (CSR) can affect firms' profitability
and increase the corporate economic burden. This current research endeavors to explore …

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to decision optimization problems in a typical ridesharing system …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …

Meta-reinforcement learning (meta-RL) aims to learn from multiple training tasks the ability
to adapt efficiently to unseen test tasks. Despite the success, existing meta-RL algorithms …

The increasing connectivity and intricate remote access environment have made traditional
perimeter-based network defense vulnerable. Zero trust becomes a promising approach to …

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to ridesharing problems. Papers on the topics of rideshare matching …

The generalization ability of most meta-reinforcement learning (meta-RL) methods is largely
limited to test tasks that are sampled from the same distribution used to sample training …

Monocular depth estimation is one of the fundamental tasks in environmental perception
and has achieved tremendous progress by virtue of deep learning. However, the …

The rapid development of artificial intelligence has further increased the level of intelligence
in the field of metal surface defect diagnosis. In general, metal surface defects are difficult to …