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

Abstract Model-based reinforcement learning algorithms with probabilistic dynamical
models are amongst the most data-efficient learning methods. This is often attributed to their …

In this work, we study model-based reinforcement learning (RL) in unknown stabilizable
linear dynamical systems. When learning a dynamical system, one needs to stabilize the …

We consider the problem of online learning in Linear Quadratic Control systems whose state
transition and state-action transition matrices $ A $ and $ B $ may be initially unknown. We …

In this paper, we study the statistical difficulty of learning to control linear systems. We focus
on two standard benchmarks, the sample complexity of stabilization, and the regret of the …

In this article, we establish regret lower bounds for adaptively controlling an unknown linear
Gaussian system with quadratic costs. We combine ideas from experiment design …

Thompson Sampling (TS) is an efficient method for decision-making under uncertainty,
where an action is sampled from a carefully prescribed distribution which is updated based …

Learning how to effectively control unknown dynamical systems is crucial for intelligent
autonomous systems. This task becomes a significant challenge when the underlying …

We study the problem of nonepisodic reinforcement learning (RL) for nonlinear dynamical
systems, where the system dynamics are unknown and the RL agent has to learn from a …

Exploration in unknown environments is a fundamental problem in reinforcement learning
and control. In this work, we study task-guided exploration and determine what precisely an …