Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …

This article surveys reinforcement learning from the perspective of optimization and control,
with a focus on continuous control applications. It reviews the general formulation …

In-context learning (ICL) is a type of prompting where a transformer model operates on a
sequence of (input, output) examples and performs inference on-the-fly. In this work, we …

A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

Abstract This work introduces Bilinear Classes, a new structural framework, which permit
generalization in reinforcement learning in a wide variety of settings through the use of …

Designing effective model-based reinforcement learning algorithms is difficult because the
ease of data generation must be weighed against the bias of model-generated data. In this …

Deep neural networks (DNNs) have demonstrated dominating performance in many fields;
since AlexNet, networks used in practice are going wider and deeper. On the theoretical …

The use of persistently exciting data has recently been popularized in the context of data-
driven analysis and control. Such data have been used to assess system-theoretic …

We consider the problem of optimal trajectory tracking for unknown systems. A novel data-
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …

Finding the minimal structural assumptions that empower sample-efficient learning is one of
the most important research directions in Reinforcement Learning (RL). This paper …