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cs.LG2025
Learn to Change the World: Multi-level Reinforcement Learning with Model-Changing Actions
Ziqing Lu, Babak Hassibi, Lifeng Lai +1
Reinforcement learning usually assumes a given or sometimes even fixed environment in which an agent seeks an optimal policy to maximize its long-term discounted reward. In contras…
cs.LG2025
Provably Invincible Adversarial Attacks on Reinforcement Learning Systems: A Rate-Distortion Information-Theoretic Approach
Ziqing Lu, Lifeng Lai, Weiyu Xu
Reinforcement learning (RL) for the Markov Decision Process (MDP) has emerged in many security-related applications, such as autonomous driving, financial decisions, and drone/robo…
math.DS2025
Efficient forward and inverse uncertainty quantification for dynamical systems based on dimension reduction and Kriging surrogate modeling in functional space
Zhouzhou Song, Weiyun Xu, Marcos A. Valdebenito +1
Surrogate models are extensively employed for forward and inverse uncertainty quantification in complex, computation-intensive engineering problems. Nonetheless, constructing high-…