Home Page

Papers

Submissions

News

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Statistics

Login

Contact Us



RSS Feed

Deep Exploration via Randomized Value Functions

Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen; 20(124):1−62, 2019.

Abstract

We study the use of randomized value functions to guide deep exploration in reinforcement learning. This offers an elegant means for synthesizing statistically and computationally efficient exploration with common practical approaches to value function learning. We present several reinforcement learning algorithms that leverage randomized value functions and demonstrate their efficacy through computational studies. We also prove a regret bound that establishes statistical efficiency with a tabular representation.

[abs][pdf][bib]       
© JMLR 2019. (edit, beta)