Abstract

Deep Reinforcement Learning has been used to exploit specific environments, but has difficulty transferring learned policies to new situations. This issue poses a problem for practical applications of Reinforcement Learning, as real-world scenarios may introduce unexpected differences that drastically reduce policy performance. We propose the use of differentiated sub-policies governed by a hierarchical controller to support adaptation in such scenarios. We also introduce a confidence- based training process for the hierarchical controller which improves training stability and convergence times. We evaluate these methods in a new Capture the Flag environment designed to explore adaptation in autonomous multi-agent settings.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11457-11463
Number of pages7
ISBN (Electronic)9781728173955
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
CountryFrance
CityParis
Period5/31/208/31/20

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Evaluating Adaptation Performance of Hierarchical Deep Reinforcement Learning'. Together they form a unique fingerprint.

Cite this