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arXiv Machine Learning By Anas Barakat, John Lazarsfeld, Georgios Piliouras, Antonios Varvitsiotis

Online Learning in Stabilized Linear Dynamical Games with Adversarial Disturbances

Read the original on arXiv Machine Learning →

The paper investigates online learning for multiple strategic agents interacting within a shared linear dynamical system that experiences adversarial disturbances. Each agent aims to minimize its own convex loss while observing the full state, and the study extends the stabilizing-baseline approach from nonstochastic online control to this multi‑agent setting. By employing online gradient descent with memory under state‑only and aggregate‑input feedback, the authors establish sublinear, near‑optimal per‑agent regret bounds and, in the common‑interest case, demonstrate equilibrium‑tracking guarantees via a time‑varying potential game framework.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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