Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-Based Toolbox (Hardcvoer)

Model-Based Reinforcement Learning

Explore a comprehensive and practical approach to reinforcement learning

Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory--optimal control and dynamic programming - or on algorithms--most of which are simulation-based.

Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework--from design to application--of a more tractable model-based reinforcement learning technique.

Model-Based Reinforcement Learning readers will also find:

• A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data
• Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning
• Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters
• An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data

Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.

《基於模型的強化學習》

探索一種全面且實用的強化學習方法

強化學習是機器學習的一個重要範式，其中智能代理執行動作以確保設備的最佳行為。儘管這種機器學習範式近年來取得了巨大的成功和普及，但以往的學術研究要麼專注於理論 - 最優控制和動態規劃，要麼專注於基於模擬的算法。

《基於模型的強化學習》提供了一個基於模型的框架，以橋接這兩個方面，從而創造出對基於模型的在線學習控制主題的整體處理。在此過程中，作者們旨在開發一個基於數據驅動的控制的基於模型的框架，以橋接數據的系統識別、基於模型的強化學習和最優控制，以及每個應用的應用。這種評估經典結果的新技術將使強化學習系統更加高效。本書的核心是提供一個從設計到應用的端到端框架，用於更易處理的基於模型的強化學習技術。

《基於模型的強化學習》讀者還將找到：

- 一本有用的教科書，適用於強調從數據中建模和控制動態系統的數據驅動和學習控制研究生課程
- 對不同技術的影響進行詳細比較，例如基本的線性二次控制器、基於學習的模型預測控制、無模型強化學習和結構化在線學習
- 地面車輛和四旋翼直升機等應用和案例研究
- 一個在線的基於Python的工具箱，該工具箱與書中涵蓋的內容以及所需的代碼和數據相配套

《基於模型的強化學習》是高年級本科生、研究生、研究助理、教授、過程控制工程師和機器人學家的有用參考資料。

Milad Farsi received the B.S. degree in Electrical Engineering (Electronics) from the University of Tabriz in 2010. He obtained his M.S. degree also in Electrical Engineering (Control Systems) from the Sahand University of Technology in 2013. Moreover, he gained industrial experience as a Control System Engineer between 2012 and 2016. Later, he acquired the Ph.D. degree in Applied Mathematics from the University of Waterloo, Canada, in 2022, and he is currently a Postdoctoral Fellow at the same institution. His research interests include control systems, reinforcement learning, and their applications in robotics and power electronics.

Jun Liu received the Ph.D. degree in Applied Mathematics from the University of Waterloo, Canada, in 2010. He is currently an Associate Professor of Applied Mathematics and a Canada Research Chair in Hybrid Systems and Control at the University of Waterloo, Canada, where he directs the Hybrid Systems Laboratory. From 2012 to 2015, he was a Lecturer in Control and Systems Engineering at the University of Sheffield. During 2011 and 2012, he was a Postdoctoral Scholar in Control and Dynamical Systems at the California Institute of Technology. His main research interests are in the theory and applications of hybrid systems and control, including rigorous computational methods for control design with applications in cyber-physical systems and robotics.

Milad Farsi於2010年從塔布里茲大學獲得電機工程（電子學）學士學位。他於2013年從Sahand科技大學獲得電機工程（控制系統）碩士學位。此外，他在2012年至2016年期間擔任控制系統工程師，獲得了工業經驗。後來，他於2022年從加拿大滑鐵盧大學獲得應用數學博士學位，目前在同一機構擔任博士後研究員。他的研究興趣包括控制系統、強化學習以及在機器人學和電力電子學中的應用。

Jun Liu於2010年從加拿大滑鐵盧大學獲得應用數學博士學位。他目前是滑鐵盧大學應用數學的副教授，並擔任加拿大研究主席，負責混合系統和控制領域的研究。他還指導混合系統實驗室。從2012年到2015年，他在謝菲爾德大學擔任控制和系統工程講師。在2011年和2012年期間，他在加州理工學院擔任控制和動力系統的博士後研究學者。他的主要研究興趣是混合系統和控制的理論和應用，包括在物聯網系統和機器人學中應用的嚴謹計算方法。