8 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…
Feature compression is the root cause of adversarial fragility in neural network classifiers
Jingchao Gao, Ziqing Lu, Raghu Mudumbai +6
In this paper, we uniquely study the adversarial robustness of deep neural networks (NN) for classification tasks against that of optimal classifiers. We look at the smallest magni…
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…