A modified Thompson sampling-based learning algorithm for unknown linear systems

We revisit the Thompson sampling-based learning algorithm for controlling an unknown linear system with quadratic cost proposed in [1]. This algorithm operates in episodes of dynamic length and it is shown to have a regret bound of $\tilde {\mathcal{O}}\left( {\sqrt T } \right)$, where T is the time-horizon. The regret bound of this algorithm is obtained under a technical assumption on the induced norm of the closed loop system. We propose a variation of this algorithm that enforces a lower bound T
min
on the episode length. We show that a careful choice of T
min
(that depends on the uncertainty about the system model) allows us to recover the $\tilde {\mathcal{O}}\left( {\sqrt T } \right)$ regret bound under a milder technical condition about the closed loop system.