Safe and Robust Reinforcement Learning: Strategies and Applications

Abstract

This paper explores the advancements in safe and robust reinforcement learning (RL), addressing the challenges and solutions associated with ensuring reliability and safety in RL systems. We review existing techniques, propose new strategies for enhancing robustness and safety, and discuss potential applications across various domains.