Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
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 gui…
Read More ↓Title Statements
- Full title :Reinforcement Learning and Approximate Dynamic Programming for Feedback Control.
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 de.
Read More ↓Edition Statements
- Publication Date:2013.
Publication Statements
- Publisher(s):Wiley
- Place of Publication:Hoboken
- Language:eng
- Publication Date:2013.
Notes
6.1.2 Markov Decision Processes.
The Physical Object
- Description:1 online resource (643 pages).
Edition Identifiers
- ISBN:1118453980, 9781118453988, 9781118453971, 1118453972, 111810420X, 9781118104200, 1299189857, 9781299189850
- DOI:10.1002/9781118453988
- oclc:ocn990660081
- LC Classification:Q325.6 .R464 2012
- Dewey Decimal:003.5
Table of Contents
Reinforcement Learning And Approximate Dynamic Programming For Feedback Control; Contents; Preface; Contributors; Part I: Feedback Control Using RL and ADP; 1. Reinforcement Learning and Approximate Dynamic Programming (RLADP)-Foundations, Common Misconceptions, and the Challenges Ahead; 1.1 Introduction; 1.2 What is RLADP?; 1.2.1 Definition of RLADP and the Task it Addresses; 1.2.2 Basic Tools-Bellman Equation, and Value and Policy Functions; 1.2.3 Optimization Over Time Without Value Functions; 1.3 Some Basic Challenges in Implementing ADP; 1.3.1 Accounting for Unseen Variables.
1.3.2 Offline Controller Design Versus Real-Time Learning1.3.3 "Model-Based" Versus "Model Free" Designs; 1.3.4 How to Approximate the Value Function Better; 1.3.5 How to Choose u(t) Based on a Value Function; 1.3.6 How to Build Cooperative Multiagent Systems with RLADP; References; 2. Stable Adaptive Neural Control of Partially Observable Dynamic Systems; 2.1 Introduction; 2.2 Background; 2.3 Stability Bias; 2.4 Example Application; 2.4.1 The Simulated System; 2.4.2 An Uncertain Linear Plant Model; 2.4.3 The Closed Loop Control System.
2.4.4 Determining RNN Weight Updates by Reinforcement Learning2.4.…
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