Offline and online time in Sequential Decision-Making Problems

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

Abstract

A connection has recently been drawn between Dynamic Optimization Problems (DOPs) and Reinforcement Learning Problems (RLPs) where they can be seen as subsets of a broader class of Sequential Decision-Making Problems (SDMPs). SDMPs require new decisions on an ongoing basis. Typically the underlying environment changes between decisions. The SDMP view is useful as it allows the unified space to be explored. Solutions can be designed for characteristics of problem instances using algorithms from either community. Little has been done on comparing algorithm performance across these communities, particularly under real-world resource constraints.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherIEEE
Number of pages8
ISBN (Electronic)978-1-5090-4240-1
DOIs
Publication statusPublished - 9 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16

Bibliographical note

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  • Cite this

    Soni, A., Lewis, P. R., & Ekárt, A. (2017). Offline and online time in Sequential Decision-Making Problems. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 [7849961] IEEE. https://doi.org/10.1109/SSCI.2016.7849961