Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

This book PDF is perfect for those who love Technology & Engineering genre, written by Frank L. Lewis and published by John Wiley & Sons which was released on 28 January 2013 with total hardcover pages 498. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Reinforcement Learning and Approximate Dynamic Programming for Feedback Control books below.

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
Author : Frank L. Lewis
File Size : 49,7 Mb
Publisher : John Wiley & Sons
Language : English
Release Date : 28 January 2013
ISBN : 9781118453971
Pages : 498 pages
Get Book

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control by Frank L. Lewis Book PDF Summary

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control by Frank L. Lewis,Derong Liu

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and

Get Book
Reinforcement Learning for Optimal Feedback Control

Reinforcement Learning for Optimal Feedback Control by Rushikesh Kamalapurkar,Patrick Walters,Joel Rosenfeld,Warren Dixon

Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use

Get Book
Reinforcement Learning and Dynamic Programming Using Function Approximators

Reinforcement Learning and Dynamic Programming Using Function Approximators by Lucian Busoniu,Robert Babuska,Bart De Schutter,Damien Ernst

From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by

Get Book
Approximate Dynamic Programming

Approximate Dynamic Programming by Warren B. Powell

A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience

Get Book
Reinforcement Learning

Reinforcement Learning by Jinna Li,Frank L. Lewis,Jialu Fan

This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics

Get Book
Reinforcement Learning and Dynamic Programming Using Function Approximators

Reinforcement Learning and Dynamic Programming Using Function Approximators by Lucian Busoniu,Robert Babuska,Bart De Schutter,Damien Ernst

From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by

Get Book
Handbook of Learning and Approximate Dynamic Programming

Handbook of Learning and Approximate Dynamic Programming by Jennie Si,Andrew G. Barto,Warren B. Powell,Don Wunsch

A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented The contributors

Get Book
Reinforcement Learning and Optimal Control

Reinforcement Learning and Optimal Control by Dimitri Bertsekas

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 intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively known by several essentially equivalent

Get Book