Certifying lower bounds for risk-sensitive reinforcement learning under adversarial state perturbations
Read the original at arxiv.org→arXiv:2609.10866v1 Announce Type: new Abstract: Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating risks in...
Original headline: "Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations"
Coverage timeline
- Sep 11, 04:00 UTC arXiv cs.LG lead source Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations