Nonparametric Additive Value Functions: Interpretable Reinforcement
  Learning with an Application to Surgical Recovery
Patrick Emedom-Nnamdi Timothy R. Smith Jukka-Pekka Onnela Junwei Lu
We propose a nonparametric additive model for estimating interpretable value
functions in reinforcement learning. Learning effective adaptive clinical
interventions that rely on digital phenotyping features is a major for concern
medical practitioners. With respect to spine surgery, different post-operative
recovery recommendations concerning patient mobilization can lead to
significant variation in patient recovery. While reinforcement learning has
achieved widespread success in domains such as games, recent methods heavily
rely on black-box methods, such neural networks. Unfortunately, these methods
hinder the ability of examining the contribution each feature makes in
producing the final suggested decision. While such interpretations are easily
provided in classical algorithms such as Least Squares Policy Iteration, basic
linearity assumptions prevent learning higher-order flexible interactions
between features. In this paper, we present a novel method that offers a
flexible technique for estimating action-value functions without making
explicit parametric assumptions regarding their additive functional form. This
nonparametric estimation strategy relies on incorporating local kernel
regression and basis expansion to obtain a sparse, additive representation of
the action-value function. Under this approach, we are able to locally
approximate the action-value function and retrieve the nonlinear, independent
contribution of select features as well as joint feature pairs. We validate the
proposed approach with a simulation study, and, in an application to spine
disease, uncover recovery recommendations that are inline with related clinical
knowledge.