Reviews: Variance Reduced Policy Evaluation with Smooth Function Approximation

Overall, the paper made significant contribution to both the reinforcement learning community and optimization community. The proposed algorithm is a variant of non-convex SAGA algorithm introduced by [1]. The novelty comes from their proof for the non-convex but strongly concave case. There are several issues which should be addressed: 1, Recasting the policy evaluation as a primal-dual optimization via the Fenchel duality technique is not new. In fact, [2,3,4] have already exploit this reformulation. First, these related work should be referred appropriately.