Dual static CVaR decompositions suffer from a CVaR evaluation gap caused by empty intersections in risk-assignment consistency constraints, and no single policy can be optimal across all risk levels in some MDPs.
Guidelines for reinforcement learning in healthcare
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A new RL framework for chronic disease management compresses time-to-control using clinician capability weighting and action intensity constraints, yielding 15 percentage point gains on synthetic type 2 diabetes simulations over standard offline RL.
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On the Fundamental Limitations of Dual Static CVaR Decompositions in Markov Decision Processes
Dual static CVaR decompositions suffer from a CVaR evaluation gap caused by empty intersections in risk-assignment consistency constraints, and no single policy can be optimal across all risk levels in some MDPs.
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Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management
A new RL framework for chronic disease management compresses time-to-control using clinician capability weighting and action intensity constraints, yielding 15 percentage point gains on synthetic type 2 diabetes simulations over standard offline RL.