Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation

Shangding Gu, Laixi Shi, Yuhao Ding +4

Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints. Howeve…