  There has been a current trend in reinforcement learning for healthcare
literature, where in order to prepare clinical datasets, researchers will carry
forward the last results of the non-administered test known as the
last-observation-carried-forward (LOCF) value to fill in gaps, assuming that it
is still an accurate indicator of the patient's current state. These values are
carried forward without maintaining information about exactly how these values
were imputed, leading to ambiguity. Our approach models this problem using
OpenAI Gym's Mountain Car and aims to address when to observe the patient's
physiological state and partly how to intervene, as we have assumed we can only
act after following an observation. So far, we have found that for a
last-observation-carried-forward implementation of the state space, augmenting
the state with counters for each state variable tracking the time since last
observation was made, improves the predictive performance of an agent,
supporting the notion of "informative missingness", and using a neural network
based Dynamics Model to predict the most probable next state value of
non-observed state variables instead of carrying forward the last observed
value through LOCF further improves the agent's performance, leading to faster
convergence and reduced variance.
