1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
A Survey of Constraint Formulations in Safe Reinforcement Learning
Akifumi Wachi, Xun Shen, Yanan Sui
Safety is critical when applying reinforcement learning (RL) to real-world problems. As a result, safe RL has emerged as a fundamental and powerful paradigm for optimizing an agent…