Abstract: Current constrained reinforcement learning (RL) methods guarantee constraint satisfaction only in expectation, which is inadequate for safety-critical decision problems. Since a constraint ...

Olivera Ciraj Bjelac, IAEA Department of Nuclear Sciences and Applications To support hospitals and specialists around the world in meeting their safety standards requirements, the IAEA has produced a ...

Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, Connecticut 06269, United States Center for Clean Energy Engineering, University of Connecticut, Storrs, ...

Abstract: In this article, we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems ...

1 College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. 2 Shenyang Aircraft Design Institute, AVIC, Shenyang, China. The paper establishes a ...

Change is the only constant in today’s rapidly evolving digital marketing landscape. Keeping up with the latest innovations isn’t just a choice – it’s a necessity for survival. Generative engine ...

1 Department of Engineering, University of Ferrara, Ferrara, Italy 2 Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy Integration between constrained optimization ...

Tsukuba, Japan—Distributed constraint optimization problems are crucial for modeling cooperative-multiagent systems. Asynchronous Distributed OPTimization (ADOPT) is a well-known algorithm for solving ...