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, which typically focus on quadratic objectives and noiseless or stochastic dynamics. Each agent seeks to minimize its own sequence of convex losses under full-state observability. Following the stabilizing-baseline paradigm in nonstochastic online control, we assume access to linear controllers that stabilize the noiseless system and focus on online adaptation by learning disturbance-action corrections. This extends adversarial online control to strategic multi-agent shared-state systems, where each agent's actions shape the state trajectory and hence the realized losses of all learners. Under state-only and aggregate-input feedback models, we analyze agents running online gradient descent with memory to update their own disturbance-action policies. We prove per-agent regret bounds that are sublinear and near-optimal in the time horizon, and show how their dependence on the number of agents varies with the feedback available to each learner. In the common-interest case, where all agents have identical cost functions, we show that the induced online problem forms a time-varying potential game and derive equilibrium-tracking guarantees. Together, these results provide a theoretical framework for adversarial online learning in stabilized linear dynamical games, connecting online control with learning in games.