Reviews: Correlation Priors for Reinforcement Learning

The paper addresses the issue of exploiting correlation structures in Markov Decision Processes with discrete state spaces. The authors identify a gap that currently makes working with discrete state spaces problematic - that there is no principled method for modelling the state correlations that is flexible enough to accommodate all the ways in which these correlations could be exploited. The paper presents a hierarchical Bayesian model and proposes a variational inference method to find solutions. The model and procedure presented in the paper are an original application of variational inference, and represent a more general method for dealing with correlation structures than anything I have encountered before. The authors have done a great job of demonstrating this by employing three vastly different problem domains. It is unusual to see Imitation Learning, System Identification and Reinforcement Learning all being tested under a new model in one paper.