Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-Based Toolbox (Hardcvoer)
暫譯: 基於模型的強化學習：從數據到連續行動的 Python 工具箱 (精裝版)

Farsi, Milad, Liu, Jun, Di Benedetto, Maria Domenica

Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-Based Toolbox (Hardcvoer)
  • 出版商: Wiley
  • 出版日期: 2022-12-28
  • 售價: $1,780
  • 貴賓價: 9.8 折 $1,744
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 111980857X
  • ISBN-13: 9781119808572
  • 相關分類: Python、程式語言、Reinforcement、DeepLearning

    • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

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，其中智能代理執行行動以確保設備的最佳行為。儘管這一機器學習範式在近年來獲得了巨大的成功和普及，但以往的研究主要集中在理論——最優控制和動態規劃——或算法上——大多數是基於模擬的。

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，從而對基於模型的在線學習控制主題進行全面的探討。在此過程中，作者旨在開發一個基於模型的數據驅動控制框架，將數據的系統識別、基於模型的強化學習和最優控制的主題及其應用相結合。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端到端的框架——從設計到應用——以實現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的讀者還將發現：


  • 一本有用的教科書，可用於強調從數據建模和控制動態系統的數據驅動和基於學習的控制的研究生課程

  • 對不同技術影響的詳細比較，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

  • 針對具有非完整動力學的地面車輛和四旋翼直升機的應用和案例研究

  • 一本與書中內容相伴的在線Python工具箱，以及必要的代碼和數據

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。

作者簡介

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.

作者簡介(中文翻譯)

Milad Farsi 於2010年獲得塔布里茲大學的電機工程（電子學）學士學位。2013年，他在薩漢德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年間擔任控制系統工程師，積累了工業經驗。隨後，他於2022年在加拿大滑鐵盧大學獲得應用數學博士學位，目前是該機構的博士後研究員。他的研究興趣包括控制系統、強化學習及其在機器人技術和電力電子學中的應用。

Jun Liu 於2010年在加拿大滑鐵盧大學獲得應用數學博士學位。目前，他是滑鐵盧大學應用數學的副教授及混合系統與控制的加拿大研究主席，並負責混合系統實驗室。2012年至2015年間，他在謝菲爾德大學擔任控制與系統工程的講師。在2011年和2012年，他是加州理工學院控制與動力系統的博士後研究員。他的主要研究興趣在於混合系統與控制的理論及應用，包括針對控制設計的嚴謹計算方法，並應用於網路物理系統和機器人技術。

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