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arXiv · 2212.12561

An active learning method for solving competitive multi-agent decision-making and control problems

Filippo Fabiani·Alberto Bemporad
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Abstract

To identify a stationary action profile for a population of competitive agents, each executing private strategies, we introduce a novel active-learning scheme where a centralized external observer (or entity) can probe the agents' reactions and recursively update simple local parametric estimates of the action-reaction mappings. Under very general working assumptions (not even assuming that a stationary profile exists), sufficient conditions are established to assess the asymptotic properties of the proposed active learning methodology so that, if the parameters characterizing the action-reaction mappings converge, a stationary action profile is achieved. Such conditions hence act also as certificates for the existence of such a profile. Extensive numerical simulations involving typical competitive multi-agent control and decision-making problems illustrate the practical effectiveness of the proposed learning-based approach.

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BibTeXRIS

Filippo Fabiani, Alberto Bemporad. 2022-12-23. An active learning method for solving competitive multi-agent decision-making and control problems. https://doi.org/10.1109/tac.2024.3477005

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