1 citations · 1 across the 5 of their papers we have counts for
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Near-Constant Strong Violation and Last-Iterate Convergence for Online CMDPs via Decaying Safety Margins
Qian Zuo, Zhiyong Wang, Fengxiang He
We study safe online reinforcement learning in Constrained Markov Decision Processes (CMDPs) under strong regret and violation metrics, which forbid error cancellation over time. E…
Integrating LTL Constraints into PPO for Safe Reinforcement Learning
Maifang Zhang, Hang Yu, Qian Zuo +3
This paper proposes Proximal Policy Optimization with Linear Temporal Logic Constraints (PPO-LTL), a framework that integrates safety constraints written in LTL into PPO for safe r…
Ensuring Safety in an Uncertain Environment: Constrained MDPs via Stochastic Thresholds
Qian Zuo, Fengxiang He
This paper studies constrained Markov decision processes (CMDPs) with constraints against stochastic thresholds, aiming at safety of reinforcement learning in unknown and uncertain…
XAI for In-hospital Mortality Prediction via Multimodal ICU Data
Xingqiao Li, Jindong Gu, Zhiyong Wang +3
Predicting in-hospital mortality for intensive care unit (ICU) patients is key to final clinical outcomes. AI has shown advantaged accuracy but suffers from the lack of explainabil…