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 …

Quantum control is concerned with active manipulation of physical and chemical processes
on the atomic and molecular scale. This work presents a perspective of progress in the field …

The ability to accurately control the dynamics of physical systems by measurement and
feedback is a pillar of modern engineering. Today, the increasing demand for applied …

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a
batch of previously collected data. This problem setting is compelling, because it offers the …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

A key aspect of robotics today is estimating the state (eg, position and orientation) of a robot,
based on noisy sensor data. This book targets students and practitioners of robotics by …

The Koopman operator is a linear but infinite-dimensional operator that governs the
evolution of scalar observables defined on the state space of an autonomous dynamical …

Abstract We introduce Embed to Control (E2C), a method for model learning and control of
non-linear dynamical systems from raw pixel images. E2C consists of a deep generative …

Correction to: Fundamentals of Spacecraft Attitude Determination and Control Page 1 Correction
to: Fundamentals of Spacecraft Attitude Determination and Control Correction to: FL Markley …

Data-driven transformations that reformulate nonlinear systems in a linear framework have
the potential to enable the prediction, estimation, and control of strongly nonlinear dynamics …