Abstract:
This paper extends recent work in feedback-based, game-theoretic optimization. We first identify limitations of existing approaches to this problem, often requiring a pri...Show MoreMetadata
Abstract:
This paper extends recent work in feedback-based, game-theoretic optimization. We first identify limitations of existing approaches to this problem, often requiring a priori knowledge to construct a nominal sensitivity model. Leveraging zero-order optimization techniques inspired by stochastic perturbation, we develop a model-free algorithm that allows agents to estimate these sensitivities during runtime, rather than a priori. We outline the convergence properties of this algorithm as a forward-backward operator-splitting technique. Finally, we compare this model-free algorithm’s performance to existing approaches, outlining its benefits and drawbacks.
Published in: 2023 European Control Conference (ECC)
Date of Conference: 13-16 June 2023
Date Added to IEEE Xplore: 17 July 2023
ISBN Information:
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