Synergies between reinforcement learning and evolutionary dynamic optimisation

Aman Soni*, Peter R. Lewis, Anikó Ekárt

*Corresponding author for this work

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

Abstract

A connection has recently been drawn between dynamic optimization and reinforcement learning problems as subsets of a broader class of sequential decision-making problems. We present a unified approach that enables the cross-pollination of ideas between established communities, and could help to develop rigorous methods for algorithm comparison and selection for real-world resource-constrained problems.

Original languageEnglish
Title of host publicationArtificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers
PublisherSpringer
Pages91-96
Number of pages6
Volume732
ISBN (Print)9783319904177
DOIs
Publication statusE-pub ahead of print - 19 Apr 2018
Event2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016 - Birmingham, United Kingdom
Duration: 14 Jun 201615 Jun 2016

Publication series

NameCommunications in Computer and Information Science
Volume732
ISSN (Print)1865-0929

Conference

Conference2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016
CountryUnited Kingdom
CityBirmingham
Period14/06/1615/06/16

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  • Cite this

    Soni, A., Lewis, P. R., & Ekárt, A. (2018). Synergies between reinforcement learning and evolutionary dynamic optimisation. In Artificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers (Vol. 732, pp. 91-96). (Communications in Computer and Information Science; Vol. 732). Springer. https://doi.org/10.1007/978-3-319-90418-4_7