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.
Off-policy deep reinforcement learning without exploration
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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.