MODEL-BASED REINFORCEMENT LEARNING: FROM DATA TO ACTIONS WITH A PYTHON-BASED TOOLBOX

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DESCRIPTION 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.

Human-Robot Interaction Control Using Reinforcement Learning

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【簡介】 A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning. 【目錄】

強化學習導論 2021 Introduction to Reinforcement Learning

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商品描述 本書特色 1.本書從假設與理論開始討論，再進入演算法與範例，讓讀者了解整體架構的來龍去脈。 2.每章的開始先綜述該章的主要內容，讓讀者有大方向的了解，再進入細節的討論。 3.相關概念以圖示方式呈現，讀者較易理解與統整。 4.每章搭配範例與程式碼，徹底了解演算法特性。 5.每章結尾作重點回顧，條列該章裡重要概念，方便讀者確認學習重點。 6.本書適用於自學、大學或科大課程教科書與實作專題的輔助教材，以及業界工程師快速奠定強化學習基礎概念與實作能力的教學書籍。 內容簡介 近年來因人工智慧興起，帶起許多學生、工程師與學者開始投入相關技術的學習、研究和開發。早期談到人工智慧，大部分會聯想到機器學習中的監督式學習和非監督式學習。然而監督或非監督式學習較難處理動態系統，機器學習技術的另一個分支─強化學習，剛好補足此缺口。強化學習的應用相當廣，最有名的兩個例子為AlphaGo透過資料學習在圍棋比賽上屢獲佳績，以及Google利用強化學習技術，優化資料中心的運作，進而減少40%的冷卻花費。本書以奠定基本功為目的，一步步帶領讀者建構完整的強化學習知識，介紹的相關概念包含：動態規劃、蒙地卡羅法、1步時間差分法、n步時間差分法、近似解法、規劃與學習、資格跡與學習、策略梯度法。 目錄大綱 第1章 強化學習框架 1-1 強化學習主要元素與馬可夫決策過程 1-2 範例1.1 1-3 策略和價值函數 1-4 範例1.2 1-5 最佳策略和最佳價值函數 重點回顧 章末練習 第2章 動態規劃 2-1 策略評估 2-2 策略改進 2-3 範例2.1與程式碼 2-4 策略疊代和價值疊代 2-5 動態規劃的優缺點與異步更新 2-6 範例2.2與程式碼 2-7 廣義策略疊代 重點回顧 章末練習 第3章 蒙地卡羅法 3-1 蒙地卡羅預測 3-2 同策略與異策略法 3-3 同策略蒙地卡羅控制 3-4 範例3.1與程式碼 3-5 異策略與重要性抽樣 3-6 異策略蒙地卡羅預測 3-7 異策略蒙地卡羅控制 重點回顧 章末練習 第4章 1步時間差分法 4-1 時間差分法 4-2 Sarsa和Q學習 4-3 範例4.1與程式碼 4-4 期望Sarsa 重點回顧 章末練習 第5章 5-1 n步時間差分預測 5-2 n步Sarsa與n步期望Sarsa 5-3 範例5.1與程式碼 5-4 異策略n步時間差分控制 重點回顧 章末練習 第6章 近似解法 6-1 函數近似與隨機梯度下降 6-2 同策略梯度與半梯度預測 6-3 同策略回合式半梯度控制 6-4 範例6.1與程式碼 6-5 異策略深度Q網路 6-6 同策略差分半梯度控制 重點回顧 章末練習 第7章 規劃與學習 7-1 規劃 7-2 範例7.1與程式碼 7-3 優先掃掠 7-4 內在動機 7-5 範例7.2與程式碼 重點回顧 章末練習 第8章 資格跡與學習 8-1 資格跡和λ報酬 8-2 半梯度TD(λ)和回合式半梯度Sarsa(λ) 8-3 資格跡和表格解法 8-4 範例8.1與程式碼 重點回顧 章末練習 第9章 策略梯度法 9-1 策略梯度與策略參數更新 9-2 簡樸策略梯度演算法 9-3 增強演算法 9-4 行動者評論家演算法 9-5 範例9.1與程式碼 重點回顧 章末練習 參考文獻 名詞索引

MULTI-AGENT COORDINATION: A REINFORCEMENT LEARNING APPROACH

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【簡介】 Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics. 【目錄】