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Many batch RL health applications first discretize time into fixed intervals.However, this discretization both loses resolution and forces a policycomputation at each (potentially fine) interval. In this work, we develop anovel framework to compress continuous trajectories into a few, interpretabledecision points --places where the batch data support multiple alternatives. Weapply our approach to create recommendations from a cohort of hypotensivepatients dataset. Our reduced state space results in faster planning and allowseasy inspection by a clinical expert.

Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment