Synergies between reinforcement learning and evolutionary dynamic optimisation

Aman Soni; Peter R. Lewis; Anikó Ekárt

doi:10.1007/978-3-319-90418-4_7

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/Published conference output › Conference publication

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 language	English
Title of host publication	Artificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers
Publisher	Springer
Pages	91-96
Number of pages	6
Volume	732
ISBN (Print)	9783319904177
DOIs	https://doi.org/10.1007/978-3-319-90418-4_7
Publication status	E-pub ahead of print - 19 Apr 2018
Event	2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016 - Birmingham, United Kingdom Duration: 14 Jun 2016 → 15 Jun 2016

Publication series

Name	Communications in Computer and Information Science
Volume	732
ISSN (Print)	1865-0929

Conference

Conference	2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016
Country/Territory	United Kingdom
City	Birmingham
Period	14/06/16 → 15/06/16

Access to Document

10.1007/978-3-319-90418-4_7

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. Advance online publication. https://doi.org/10.1007/978-3-319-90418-4_7

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title = "Synergies between reinforcement learning and evolutionary dynamic optimisation",

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.",

author = "Aman Soni and Lewis, {Peter R.} and Anik{\'o} Ek{\'a}rt",

year = "2018",

month = apr,

day = "19",

doi = "10.1007/978-3-319-90418-4_7",

language = "English",

isbn = "9783319904177",

volume = "732",

series = "Communications in Computer and Information Science",

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booktitle = "Artificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers",

address = "Germany",

note = "2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016 ; Conference date: 14-06-2016 Through 15-06-2016",

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Soni, A, Lewis, PR & 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, Communications in Computer and Information Science, vol. 732, Springer, pp. 91-96, 2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016, Birmingham, United Kingdom, 14/06/16. https://doi.org/10.1007/978-3-319-90418-4_7

Synergies between reinforcement learning and evolutionary dynamic optimisation. / Soni, Aman; Lewis, Peter R.; Ekárt, Anikó.
Artificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers. Vol. 732 Springer, 2018. p. 91-96 (Communications in Computer and Information Science; Vol. 732).

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Lewis, Peter R.

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AB - 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.

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Synergies between reinforcement learning and evolutionary dynamic optimisation

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