Deep reinforcement learning that matters

Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

833 Scopus citations

Abstract

In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages3207-3214
Number of pages8
ISBN (Electronic)9781577358008
StatePublished - 2018
Externally publishedYes
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Conference

Conference32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Country/TerritoryUnited States
CityNew Orleans
Period2/2/182/7/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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