@inproceedings{a9ccd6b56e994127a7ea5630b5b2ed91,
title = "Graying the black box: Understanding DQNs",
abstract = "In recent years there is a growing interest in using deep representations for reinforcement learning. In this paper, we present a methodology and tools to analyze Deep Q-networks (DQNs) in a non-blind matter. Using our tools we reveal that the features learned by DQNs aggregate the state space in a hierarchical fashion, explaining its success. Moreover we are able to understand and describe the policies learned by DQNs for three different Atari2600 games and suggest ways to interpret, debug and optimize deep neural networks in reinforcement learning.",
author = "Tom Zahavy and Zrihem, {Nir Ben} and Shie Mannor",
note = "Publisher Copyright: {\textcopyright} 2016 by the author(s).; 33rd International Conference on Machine Learning, ICML 2016 ; Conference date: 19-06-2016 Through 24-06-2016",
year = "2016",
language = "الإنجليزيّة",
series = "33rd International Conference on Machine Learning, ICML 2016",
pages = "2809--2822",
editor = "Weinberger, {Kilian Q.} and Balcan, {Maria Florina}",
booktitle = "33rd International Conference on Machine Learning, ICML 2016",
}