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This paper studies online solutions for regret-optimal control in partiallyobservable systems over an infinite-horizon. Regret-optimal control aims tominimize the difference in LQR cost between causal and non-causal controllerswhile considering the worst-case regret across all $\ell_2$-norm-boundeddisturbance and measurement sequences. Building on ideas from Sabag et al.,2023, on the the full-information setting, our work extends the framework tothe scenario of partial observability (measurement-feedback). We derive anexplicit state-space solution when the non-causal solution is the one thatminimizes the $\mathcal H_2$ criterion, and demonstrate its practical utilityon several practical examples. These results underscore the framework'ssignificant relevance and applicability in real-world systems.

Regret-Optimal Control under Partial Observability