Amortized Variational Deep Q Network

TL;DR

This paper introduces an amortized variational approach for Deep Q Networks that improves exploration efficiency, reduces parameters, and outperforms existing methods in classical control tasks with less training time.

Contribution

It proposes a novel amortized variational inference framework for Deep Q Networks, balancing exploration and exploitation with fewer parameters and enhanced performance.

Findings

Outperforms state-of-the-art methods in control tasks

Requires significantly less training time

Uses fewer learning parameters

Abstract

Efficient exploration is one of the most important issues in deep reinforcement learning. To address this issue, recent methods consider the value function parameters as random variables, and resort variational inference to approximate the posterior of the parameters. In this paper, we propose an amortized variational inference framework to approximate the posterior distribution of the action value function in Deep Q Network. We establish the equivalence between the loss of the new model and the amortized variational inference loss. We realize the balance of exploration and exploitation by assuming the posterior as Cauchy and Gaussian, respectively in a two-stage training process. We show that the amortized framework can results in significant less learning parameters than existing state-of-the-art method. Experimental results on classical control tasks in OpenAI Gym and chain Markov Decision Process tasks show that the proposed method performs significantly better than state-of-art methods and requires much less training time.