8 citations · 14 across the 8 of their papers we have counts for
8 papers
Constrained Reinforcement Learning Under Model Mismatch
Zhongchang Sun, Sihong He, Fei Miao +1
Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may ea…
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
Qi Zhang, Yi Zhou, Ashley Prater-Bennette +2
Distributionally robust optimization (DRO) is a powerful framework for training robust models against data distribution shifts. This paper focuses on constrained DRO, which has an…
Sample Complexity Characterization for Linear Contextual MDPs
Junze Deng, Yuan Cheng, Shaofeng Zou +1
Contextual Markov decision processes (CMDPs) describe a class of reinforcement learning problems in which the transition kernels and reward functions can change over time with diff…
Quickest Change Detection in Autoregressive Models
Zhongchang Sun, Shaofeng Zou
The problem of quickest change detection (QCD) in autoregressive (AR) models is investigated. A system is being monitored with sequentially observed samples. At some unknown time,…
Robust Multi-Agent Reinforcement Learning with State Uncertainty
Sihong He, Songyang Han, Sanbao Su +3
In real-world multi-agent reinforcement learning (MARL) applications, agents may not have perfect state information (e.g., due to inaccurate measurement or malicious attacks), whic…
Model-Free Robust Average-Reward Reinforcement Learning
Yue Wang, Alvaro Velasquez, George Atia +2
Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In this paper, we focus…