Social Imitation in Cooperative Multiarmed Bandits: Partition-Based Algorithms with Strictly Local Information

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

2 Citations (Scopus)

Abstract

We study distributed cooperative decision-making in a multi-agent stochastic multi-armed bandit (MAB) problem in which agents are connected through an undirected graph and observe the actions and rewards of their neighbors. We develop a novel policy based on partitions of the communication graph and propose a distributed method for selecting an arbitrary number of leaders and partitions. We analyze this new policy and evaluate its performance using Monte-Carlo simulations.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5239-5244
Number of pages6
ISBN (Electronic)9781538613955
DOIs
StatePublished - Jan 18 2019
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2018-December

Conference

Conference57th IEEE Conference on Decision and Control, CDC 2018
CountryUnited States
CityMiami
Period12/17/1812/19/18

Fingerprint

Decision making
Communication
Monte Carlo simulation

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Control and Systems Engineering
  • Modeling and Simulation

Cite this

Ehrich Leonard, N. (2019). Social Imitation in Cooperative Multiarmed Bandits: Partition-Based Algorithms with Strictly Local Information. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 5239-5244). [8619744] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619744
Ehrich Leonard, Naomi. / Social Imitation in Cooperative Multiarmed Bandits : Partition-Based Algorithms with Strictly Local Information. 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5239-5244 (Proceedings of the IEEE Conference on Decision and Control).
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Ehrich Leonard, N 2019, Social Imitation in Cooperative Multiarmed Bandits: Partition-Based Algorithms with Strictly Local Information. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619744, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 5239-5244, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8619744

Social Imitation in Cooperative Multiarmed Bandits : Partition-Based Algorithms with Strictly Local Information. / Ehrich Leonard, Naomi.

2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5239-5244 8619744 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

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

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Ehrich Leonard N. Social Imitation in Cooperative Multiarmed Bandits: Partition-Based Algorithms with Strictly Local Information. In 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5239-5244. 8619744. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2018.8619744