  The main challenge for adaptive regulation of linear-quadratic systems is the
trade-off between identification and control. An adaptive policy needs to
address both the estimation of unknown dynamics parameters (exploration), as
well as the regulation of the underlying system (exploitation). To this end,
optimism-based methods which bias the identification in favor of optimistic
approximations of the true parameter are employed in the literature. A number
of asymptotic results have been established, but their finite time counterparts
are few, with important restrictions.
  This study establishes results for the worst-case regret of optimism-based
adaptive policies. The presented high probability upper bounds are optimal up
to logarithmic factors. The non-asymptotic analysis of this work requires very
mild assumptions; (i) stabilizability of the system's dynamics, and (ii)
limiting the degree of heaviness of the noise distribution. To establish such
bounds, certain novel techniques are developed to comprehensively address the
probabilistic behavior of dependent random matrices with heavy-tailed
distributions.
