Title: A short variational proof of equivalence between policy gradients and soft Q learning,
Abstract: Two main families of reinforcement learning algorithms, Q-learning and policy
gradients, have recently been proven to be equivalent when using a softmax
relaxation on one part, and an entropic regularization on the other. We relate
this result to the well-known convex duality of Shannon entropy and the softmax
function. Such a result is also known as the Donsker-Varadhan formula. This
provides a short proof of the equivalence. We then interpret this duality
further, and use ideas of convex analysis to prove a new policy inequality
relative to soft Q-learning.