Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …

The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …

This book considers large and challenging multistage decision problems, which can be
solved in principle by dynamic programming (DP), but their exact solution is computationally …

With the rapid development of computer technology, automatic control technology and
communication technology, research on unmanned aerial vehicles (UAVs) has attracted …

This paper reviews the current state of the art on reinforcement learning (RL)-based
feedback control solutions to optimal regulation and tracking of single and multiagent …

This paper presents OptNet, a network architecture that integrates optimization problems
(here, specifically in the form of quadratic programs) as individual layers in larger end-to-end …

Control in the presence of uncertainty is one of the main topics of modern control theory. In
the formulation of any control problem there is always a discrepancy between the actual …

This article provides a historical perspective of the field of adaptive control over the past
seven decades and its intersection with learning. A chronology of key events over this large …

This paper develops a new framework to analyze and design iterative optimization
algorithms built on the notion of integral quadratic constraints (IQCs) from robust control …

A new way to design parameter estimators with enhanced performance is proposed in the
paper. The procedure consists of two stages, first, the generation of new regression forms …