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Online Learning in Stabilized Linear Dynamical Games with Adversarial Disturbances
We study online learning in linear dynamical games where multiple strategic agents act on a shared state evolving according to a linear dynamical system subject to adversarial disturbances. This setting lies beyond both single-agent nonstochastic online control and classical linear-quadratic games,
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