Reviews: Correlation Priors for Reinforcement Learning

The paper develops a variational inference algorithm for modelling discrete state action MDPs. The model can be used to capture the correlations inherent between the states of an MDP. For doing so, Polya-Gamma auxiliary variables have been used, which has been proposed before. The contribution of the paper is the variational inference algorithm instead of using Block-based Gibbs sampling as in the original model. The paper attacks a very important problem, follows a nice idea and is well executed.