The multi-armed bandit with stochastic plays

Antoine Lesage-Landry; Joshua A. Taylor

doi:10.1109/TAC.2017.2765501

The multi-armed bandit with stochastic plays

Antoine Lesage-Landry, Joshua A. Taylor

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

We extend the stochastic multi-armed bandit to the case where the number of arms to play evolves as a stationary process. Our work is motivated by demand response in power systems, in which the number of arms to play, or loads to dispatch, depends on a random power imbalance. We give an upper confidence bound-based algorithm that achieves sublinear pseudo-regret. We apply our results in several examples from demand response.

Original language	English (US)
Pages (from-to)	2280-2286
Number of pages	7
Journal	IEEE Transactions on Automatic Control
Volume	63
Issue number	7
DOIs	https://doi.org/10.1109/TAC.2017.2765501
State	Published - Jul 2018
Externally published	Yes

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Keywords

Demand response
multi-armed bandit (MAB)
online learning
stochastic bandit

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10.1109/TAC.2017.2765501

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abstract = "We extend the stochastic multi-armed bandit to the case where the number of arms to play evolves as a stationary process. Our work is motivated by demand response in power systems, in which the number of arms to play, or loads to dispatch, depends on a random power imbalance. We give an upper confidence bound-based algorithm that achieves sublinear pseudo-regret. We apply our results in several examples from demand response.",

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AB - We extend the stochastic multi-armed bandit to the case where the number of arms to play evolves as a stationary process. Our work is motivated by demand response in power systems, in which the number of arms to play, or loads to dispatch, depends on a random power imbalance. We give an upper confidence bound-based algorithm that achieves sublinear pseudo-regret. We apply our results in several examples from demand response.

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KW - online learning

KW - stochastic bandit

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The multi-armed bandit with stochastic plays

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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