Fuzzy Reinforcement Learning Based Controller Design : Lyapunov Theory based Reinforcement Learning Controller for Non Linear Systems

Abhishek Kumar

ISBN 10: 6204732013 / ISBN 13: 9786204732015

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - This book aims at providing basic introduction about reinforcement learning (RL), application of RL as controller and introducing stability in RL. Q-learning is most widely used RL technique and is explained in detail in this book. In RL, 'Curse of Dimensionality' is a major issue and author has used fuzzy inference system to handle this problem resulting in Fuzzy Q learning. Reinforcement learning works on exploitation and exploration policy and hence RL based controller may face stability issue. The main emphasis of this book is to introduce stability in RL based controller using Lyapunov theory. The proposed RL based controllers are simulated on various nonlinear systems including Inverted Pendulum and Robotic Manipulator.

Seller Inventory # 9786204732015

Bibliographic Details

• Title: Fuzzy Reinforcement Learning Based Controller Design : Lyapunov Theory based Reinforcement Learning Controller for Non Linear Systems
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6204732013
• ISBN 13: 9786204732015
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 102 grams

About this title

Synopsis

This book aims at providing basic introduction about reinforcement learning (RL), application of RL as controller and introducing stability in RL. Q-learning is most widely used RL technique and is explained in detail in this book. In RL, “Curse of Dimensionality” is a major issue and author has used fuzzy inference system to handle this problem resulting in Fuzzy Q learning. Reinforcement learning works on exploitation and exploration policy and hence RL based controller may face stability issue. The main emphasis of this book is to introduce stability in RL based controller using Lyapunov theory. The proposed RL based controllers are simulated on various nonlinear systems including Inverted Pendulum and Robotic Manipulator.