Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics. (arXiv:2203.01170v2 [math.OC] UPDATED)

We consider the problem of controlling an unknown linear dynamical system
under a stochastic convex cost and full feedback of both the state and cost
function. We present a computationally efficient algorithm that attains an
optimal $\sqrt{T}$ regret-rate compared to the best stabilizing linear
controller in hindsight. In contrast to previous work, our algorithm is based
on the Optimism in the Face of Uncertainty paradigm. This results in a
substantially improved computational complexity and a simpler analysis.

arxiv costs dynamics linear math stochastic