Online Control via Counterfactual Tracking
We study online control of a known linear dynamical system with adversarial costs and bounded disturbances, measuring regret against a general class of benchmark policies. We introduce counterfactual tracking, which separates the challenge of learning from the challenge of controlling the system. An online learner builds a reference trajectory by selecting or averaging the trajectories that the benchmark policies would have generated under the realized costs and disturbances, and a corrective law steers the system toward that reference. Charging each change in the reference its recovery cost (the cost of steering the system onto the new reference) reduces the problem to online learning wi...
| Authors | Yunzong Xu |
|---|---|
| Primary category | math.OC |
| Published | 14 July 2026 |
| Last updated at source | 2 October 2026 |
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