Safe and Robust Reinforcement-Learning: Principles and Practice

arXiv:2403.18539v1 Announce Type: new
Abstract: Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges related to safety and robustness. This paper aims to identify and further understand those challenges thorough the exploration of the main dimensions of the safe and robust RL landscape, encompassing algorithmic, ethical, and practical considerations. We conduct a comprehensive review of methodologies and open problems that summarizes the efforts in recent …

abstract arxiv challenges cs.lg cs.sy deployment dimensions eess.sy exploration identify paper practice reinforcement reinforcement learning robust robustness safety success systems tasks type world