8 citations · 11 across the 7 of their papers we have counts for
6 papers · 1 filter
Robust Risk-Sensitive Reinforcement Learning from Corrupted Human Feedback
Xinyi Ni, Lifeng Lai
Reinforcement learning with human feedback (RLHF) learns from human comparisons, which can be corrupted or deliberately manipulated. This paper studies online risk-sensitive RLHF w…
Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk
Xinyi Ni, Lifeng Lai
Robust Markov Decision Processes (RMDPs) have received significant research interest, offering an alternative to standard Markov Decision Processes (MDPs) that often assume fixed t…
Minimax Optimal Q Learning with Nearest Neighbors
Puning Zhao, Lifeng Lai
Analyzing the Markov decision process (MDP) with continuous state spaces is generally challenging. A recent interesting work \cite{shah2018q} solves MDP with bounded continuous sta…
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty
Guanlin Liu, Zhihan Zhou, Han Liu +1
Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties. In this paper, we focus on action robust RL with th…
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning
Guanlin Liu, Lifeng Lai
Due to the broad range of applications of multi-agent reinforcement learning (MARL), understanding the effects of adversarial attacks against MARL model is essential for the safe a…
Efficiently Escaping Saddle Points in Bilevel Optimization
Minhui Huang, Xuxing Chen, Kaiyi Ji +2
Bilevel optimization is one of the fundamental problems in machine learning and optimization. Recent theoretical developments in bilevel optimization focus on finding the first-ord…