|
| |
|
| Reinforcement Learning, Theory and Applications |
ISBN 978-3-902613-14-1
hard cover, 424 pages
Edited by: Cornelius Weber, Mark Elshaw and Norbert Michael Mayer
Publisher: I-Tech Education and Publishing, Vienna, Austria
Publication date: January 2008
Price: 80 Euro incl. package & postage
|
| |Download the full text of this book| |
|
| |
|
About the Book
Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal.
The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field. |
|
Table of Contents
01Neural Forecasting Systems
Takashi Kuremoto, Masanao Obayashi and Kunikazu Kobayashi
02Reinforcement Learning in System Identification
Mariela Cerrada and Jose Aguilar
03Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design
Cheng-Jian Lin
04Superposition-Inspired Reinforcement Learning and Quantum Reinforcement Learning
Chun-Lin Chen and Dao-Yi Dong
05An Extension of Finite-state Markov Decision Process and an Application of Grammatical Inference
Takeshi Shibata and Ryo Yoshinaka
06Interaction between the Spatio-Temporal Learning Rule (non Hebbian) and Hebbian in Single Cells: A Cellular Mechanism of Reinforcement Learning
Minoru Tsukada
07Reinforcement Learning Embedded in Brains and Robots
Cornelius Weber, Mark Elshaw, Stefan Wermter, Jochen Triesch and Christopher Willmot
08Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems
Jim Dowling and Seif Haridi
09Multi-Automata Learning
Verbeeck Katja, Nowe Ann, Vrancx Peter and Peeters Maarten
10Abstraction for Genetics-based Reinforcement Learning
Will Browne, Dan Scott and Charalambos Ioannides
11Dynamics of the Bush-Mosteller Learning Algorithm in 2x2 Games
Luis R. Izquierdo and Segismundo S. Izquierdo
12Modular Learning Systems for Behavior Acquisition in Multi-Agent Environment
Yasutake Takahashi and Minoru Asada
13Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradigm
Olivier Pietquin
14Water Allocation Improvement in River Basin Using Adaptive Neural Fuzzy Reinforcement Learning Approach
Abolpour B., Javan M. and Karamouz M.
15Reinforcement Learning for Building Environmental Control
Konstantinos Dalamagkidis and Dionysia Kolokotsa
16Model-Free Learning Control of Chemical Processes
S. Syafiie, F. Tadeo and E. Martinez
17Reinforcement Learning-Based Supervisory Control Strategy for a Rotary Kiln Process
Xiaojie Zhou, Heng Yue and Tianyou Chai
18Inductive Approaches based on Trial/Error Paradigm for Communications Network
Mellouk Abdelhamid
19The Allocation of Time and Location Information to Activity-Travel Sequence Data by means of Reinforcement Learning
Wets Janssens
20Application on Reinforcement Learning for Diagnosis based on Medical Image
Stelmo Magalhaes Barros Netto, Vanessa Rodrigues Coelho Leite, Aristofanes Correa Silva, Anselmo Cardoso de Paiva and Areolino de Almeida Neto
21RL based Decision Support System for u-Healthcare Environment
Devinder Thapa, In-Sung Jung, and Gi-Nam Wang
22Reinforcement Learning to Support Meta-Level Control in Air Traffic Management
Daniela P. Alves, Li Weigang and Bueno B. Souza
