Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only
maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a …

We adapt recent tools developed for the analysis of Stochastic Gradient Descent (SGD) in
non-convex optimization to obtain convergence and sample complexity guarantees for the …

Improving the sample efficiency in reinforcement learning has been a long-standing
research problem. In this work, we aim to reduce the sample complexity of existing policy …

Modern policy optimization methods in reinforcement learning, such as TRPO and PPO, owe
their success to the use of parameterized policies. However, while theoretical guarantees …

Abstract Proximal Policy Optimization (PPO) is an important reinforcement learning method,
which has achieved great success in sequential decision-making problems. However, PPO …

Wastewater treatment plants (WWTPs) increasingly utilize sensors to optimize operations
and ensure treated water quality. These sensors' rich datasets are well-suited for automated …

In an era dominated by digital communication, the vast amounts of data generated from
social media and financial markets present unique opportunities and challenges for …

Abstract In seismic safety, Earthquake Early Warning (EEW) systems are indispensable for
mitigating earthquake hazards. These systems strive to quickly evaluate earthquake …