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Off-policy reinforcement learning enables near-optimal policy from suboptimalexperience, thereby provisions opportunity for artificial intelligenceapplications in healthcare. Previous works have mainly framed patient-clinicianinteractions as Markov decision processes, while true physiological states arenot necessarily fully observable from clinical data. We capture this situationwith partially observable Markov decision process, in which an agent optimisesits actions in a belief represented as a distribution of patient statesinferred from individual history trajectories. A Gaussian mixture model isfitted for the observed data. Moreover, we take into account the fact thatnuance in pharmaceutical dosage could presumably result in significantlydifferent effect by modelling a continuous policy through a Gaussianapproximator directly in the policy space, i.e. the actor. To address thechallenge of infinite number of possible belief states which renders exactvalue iteration intractable, we evaluate and plan for only every encounteredbelief, through heuristic search tree by tightly maintaining lower and upperbounds of the true value of belief. We further resort to functionapproximations to update value bounds estimation, i.e. the critic, so that thetree search can be improved through more compact bounds at the fringe nodesthat will be back-propagated to the root. Both actor and critic parameters arelearned via gradient-based approaches. Our proposed policy trained from realintensive care unit data is capable of dictating dosing on vasopressors andintravenous fluids for sepsis patients that lead to the best patient outcomes.

The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making