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