6 papers
Dissimilarity-Based Markov Decision Processes for Stabilizing State Distributions
Xun Shen, Yuepeng Wang, Daniyal Sajedi-Hosseini +6
Reinforcement learning for stochastic systems typically optimizes expected cumulative rewards, while reliable and repeatable behavior also depends on how the state distribution evo…
Interaction-Limited Safe Continuous-Time RL for Dynamical Medical Treatment
Xun Shen, Yuepeng Wang, Akifumi Wachi +13
Dynamic medical treatment requires deciding treatment intensity and intervention timing, while patient states evolve continuously and adverse events may occur between clinical inte…
A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning
Yuepeng Wang, Ken Kawano, Yongqi Zhou +12
Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performe…
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies
Runze Yan, Xun Shen, Akifumi Wachi +3
When applying offline reinforcement learning (RL) in healthcare scenarios, the out-of-distribution (OOD) issues pose significant risks, as inappropriate generalization beyond clini…
Flipping-based Policy for Chance-Constrained Markov Decision Processes
Xun Shen, Shuo Jiang, Akifumi Wachi +2
Safe reinforcement learning (RL) is a promising approach for many real-world decision-making problems where ensuring safety is a critical necessity. In safe RL research, while expe…
Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties
Xun Shen, Ye Wang, Kazumune Hashimoto +2
Validating and controlling safety-critical systems in uncertain environments necessitates probabilistic reachable sets of future state evolutions. The existing methods of computing…