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Variational Deep Q Network

2017-11-30Code Available0· sign in to hype

Yunhao Tang, Alp Kucukelbir

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Abstract

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).

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