Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

This paper addresses the optimal control problem known as the linear quadratic regulator in
the case when the dynamics are unknown. We propose a multistage procedure, called …

We consider the problem of online adaptive control of the linear quadratic regulator, where
the true system parameters are unknown. We prove new upper and lower bounds …

We study the performance of the certainty equivalent controller on Linear Quadratic (LQ)
control problems with unknown transition dynamics. We show that for both the fully and …

We consider reinforcement learning (RL) in continuous time with continuous feature and
action spaces. We motivate and devise an exploratory formulation for the feature dynamics …

We study derivative-free methods for policy optimization over the class of linear policies. We
focus on characterizing the convergence rate of these methods when applied to linear …

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 …

The effectiveness of model-based versus model-free methods is a long-standing question in
reinforcement learning (RL). Motivated by recent empirical success of RL on continuous …

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 …

Thompson sampling has been widely used for contextual bandit problems due to the
flexibility of its modeling power. However, a general theory for this class of methods in the …