  We propose a framework that directly tackles the probability distribution of
the value function parameters in Deep Q Network (DQN), with powerful
variational inference subroutines to approximate the posterior of the
parameters. We will establish the equivalence between our proposed surrogate
objective and variational inference loss. Our new algorithm achieves efficient
exploration and performs well on large scale chain Markov Decision Process
(MDP).
