We present the first computationally-efficient algorithm with $\widetilde {O}(\sqrt {T}) $ regret
for learning in Linear Quadratic Control systems with unknown dynamics. By that, we resolve …

This work studies the problem of sequential control in an unknown, nonlinear dynamical
system, where we model the underlying system dynamics as an unknown function in a …

We consider the problem of controlling a possibly unknown linear dynamical system with
adversarial perturbations, adversarially chosen convex loss functions, and partially …

We consider the problem of controlling an unknown linear dynamical system in the presence
of (nonstochastic) adversarial perturbations and adversarial convex loss functions. In …

We study the problem of system identification and adaptive control in partially observable
linear dynamical systems. Adaptive and closed-loop system identification is a challenging …

We consider the problem of black-box control: the task of controlling an unknown linear time-
invariant dynamical system from a single trajectory without a stabilizing controller. Under the …

Weconsider the problem of learning a realization for a linear time-invariant (LTI) dynamical
system from input/output data. Given a single input/output trajectory, we provide finite time …

Abstract We study Reinforcement Learning for partially observable systems using function
approximation. We propose a new PO-bilinear framework, that is general enough to include …

The linear quadratic regulator (LQR) problem has reemerged as an important theoretical
benchmark for reinforcement learning-based control of complex dynamical systems with …

We introduce algorithms for learning nonlinear dynamical systems of theform $ x_ {t+
1}=\sigma (\Theta {} x_t)+\varepsilon_t $, where $\Theta $ is a weightmatrix, $\sigma $ is a …