88 citations · 605 across the 80 of their papers we have counts for
84 papers
History Matters: Meta-policy Delegation with Heterogeneous Multi-agent Reinforcement Learning
Ziqing Lu, Avinash Reddy Mudireddy, Sarra Alqahtani +1
AI agents are expected to play an increasingly important role in future decision-making systems. In this paper, we consider collaborative systems composed of heterogeneous multi-ag…
Efficient Preference Poisoning Attack on Offline RLHF
Chenye Yang, Weiyu Xu, Lifeng Lai
Offline Reinforcement Learning from Human Feedback (RLHF) pipelines such as Direct Preference Optimization (DPO) train on a pre-collected preference dataset, which makes them vulne…
Artificial Superintelligence May be Useless: Equilibria in the Economy of Multiple AI Agents
Huan Cai, Ziqing Lu, Catherine Xu +2
With recent development of artificial intelligence, it is more common to adopt AI agents in economic activities. This paper explores the economic actions of agents, including human…
Repair Brain Damage: Real-Numbered Error Correction Code for Neural Network
Ziqing Li, Myung Cho, Qiutong Jin +1
We consider a neural network (NN) that may experience memory faults and computational errors. In this paper, we propose a novel real-number-based error correction code (ECC) capabl…
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…
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…