Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
This provides the first computationally efficient confidence-based algorithm for LQR with worst-case optimal regret guarantees, addressing a bottleneck in control theory.
The paper tackles the exploration-exploitation dilemma in linear quadratic regulators (LQR) by proposing a computationally efficient algorithm based on Lagrangian relaxation, achieving an ε-optimistic controller with O(log(1/ε)) Riccati equation solves and recovering Õ(√T) regret.
We study the exploration-exploitation dilemma in the linear quadratic regulator (LQR) setting. Inspired by the extended value iteration algorithm used in optimistic algorithms for finite MDPs, we propose to relax the optimistic optimization of \ofulq and cast it into a constrained \textit{extended} LQR problem, where an additional control variable implicitly selects the system dynamics within a confidence interval. We then move to the corresponding Lagrangian formulation for which we prove strong duality. As a result, we show that an $ε$-optimistic controller can be computed efficiently by solving at most $O\big(\log(1/ε)\big)$ Riccati equations. Finally, we prove that relaxing the original \ofu problem does not impact the learning performance, thus recovering the $\tilde{O}(\sqrt{T})$ regret of \ofulq. To the best of our knowledge, this is the first computationally efficient confidence-based algorithm for LQR with worst-case optimal regret guarantees.