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There has been a current trend in reinforcement learning for healthcareliterature, where in order to prepare clinical datasets, researchers will carryforward the last results of the non-administered test known as thelast-observation-carried-forward (LOCF) value to fill in gaps, assuming that itis still an accurate indicator of the patient's current state. These values arecarried forward without maintaining information about exactly how these valueswere imputed, leading to ambiguity. Our approach models this problem usingOpenAI Gym's Mountain Car and aims to address when to observe the patient'sphysiological state and partly how to intervene, as we have assumed we can onlyact after following an observation. So far, we have found that for alast-observation-carried-forward implementation of the state space, augmentingthe state with counters for each state variable tracking the time since lastobservation was made, improves the predictive performance of an agent,supporting the notion of "informative missingness", and using a neural networkbased Dynamics Model to predict the most probable next state value ofnon-observed state variables instead of carrying forward the last observedvalue through LOCF further improves the agent's performance, leading to fasterconvergence and reduced variance.

Exploring Optimal Control With Observations at a Cost