Advances in Reinforcement Learning

Abdelhamid Mellouk

• Publisher: IntechOpen
• Year: 2011
• Print ISBN: 9789533073699
• eBook ISBN: 9789535155034
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.

Table of contents

• Advances in Reinforcement Learning
• Contents
• Preface
• Chapter 1 Wireless Networks Inductive Routing Based on Reinforcement Learning Paradigms
• Chapter 2 Cooperative Agent Learning Model in Multi-cluster Grid
• Chapter 3 A Reinforcement Learning Approach to Intelligent Goal Coordination of Two-Level Large-Scale Control Systems
• Chapter 4 Reinforcement Learning of User Preferences for a Ubiquitous Personal Assistant
• Chapter 5 Cooperative Behavior Rule Acquisition for Multi-Agent Systems by Machine Learning
• Chapter 6 Emergence of Intelligence Through Reinforcement Learning with a Neural Network
• Chapter 7 Reinforcement Learning using Kohonen Feature Map Probabilistic Associative Memory based on Weights Distribution
• Chapter 8 How to Recommend Preferable Solutions of a User in Interactive Reinforcement Learning?
• Chapter 9 Reward Prediction Error Computation in the Pedunculopontine Tegmental Nucleus Neurons
• Chapter 10 Subgoal Identifications in Reinforcement Learning: A Survey
• Chapter 11 A Reinforcement Learning System Embedded Agent with Neural Network-Based Adaptive Hierarchical Memory Structure
• Chapter 12 Characterization of Motion Forms of Mobile Robot Generated in Q-Learning Process
• Chapter 13 A Robot Visual Homing Model that Traverses Conjugate Gradient TD to a Variable λ TD and Uses Radial Basis Features
• Chapter 14 Complex-Valued Reinforcement Learning: A Context-based Approach for POMDPs
• Chapter 15 Adaptive PID Control of a Nonlinear Servomechanism Using Recurrent Neural Networks
• Chapter 16 Robotic Assembly Replanning Agent Based on Neural Network Adjusted Vibration Parameters
• Chapter 17 Integral Reinforcement Learning for Finding Online the Feedback Nash Equilibrium of Nonzero-Sum Differential Games
• Chapter 18 Online Gaming: Real Time Solution of Nonlinear Two-Player Zero-Sum Games Using Synchronous Policy Iteration
• Chapter 19 Hybrid Intelligent Algorithm for Flexible Job-Shop Scheduling Problem under Uncertainty
• Chapter 20 Adaptive Critic Designs-Based Autonomous Unmanned Vehicles Navigation: Application to Robotic Farm Vehicles
• Chapter 21 DAQL-Enabled Autonomous Vehicle Navigation in Dynamically Changing Environment
• Chapter 22 An Intelligent Marshaling Based on Transfer Distance of Containers Using a New Reinforcement Learning for Logistics
• Chapter 23 Distributed Parameter Bioprocess Plant Identification and I-Term Control Using Decentralized Fuzzy-Neural Multi-Models
• Chapter 24 Optimal Cardiac Pacing with Q Learning