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Reinforcement Learning
Edited by Cornelius Weber, Mark Elshaw and Norbert Michael Mayer, ISBN 978-3-902613-14-1, Hard cover, 424 pages, Publisher: I-Tech Education and Publishing, Published: January 01, 2008 under CC BY-NC-SA 3.0 license, in subject Artificial Intelligence
DOI: 10.5772/54
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.
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Book contents
- Chapter 1Neural Forecasting Systems
- Chapter 2Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradigm
- Chapter 3Water Allocation Improvement in River Basin Using Adaptive Neural Fuzzy Reinforcement Learning Approach
- Chapter 4Reinforcement Learning for Building Environmental Control
- Chapter 5Model-Free Learning Control of Chemical Processes
- Chapter 6Reinforcement Learning-Based Supervisory Control Strategy for a Rotary Kiln Process
- Chapter 7Inductive Approaches Based on Trial/Error Paradigm for Communications Network
- Chapter 8The Allocation of Time and Location Information to Activity-Travel Sequence Data by Means of Reinforcement Learning
- Chapter 9Application on Reinforcement Learning for Diagnosis Based on Medical Image
- Chapter 10RL Based Decision Support System for u-Healthcare Environment
- Chapter 11Modular Learning Systems for Behavior Acquisition in Multi-Agent Environment
- Chapter 12Dynamics of the Bush-Mosteller Learning Algorithm in 2x2 Games
- Chapter 13Reinforcement Learning in System Identification
- Chapter 14Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design
- Chapter 15Superposition-Inspired Reinforcement Learning and Quantum Reinforcement Learning
- Chapter 16An Extension of Finite-state Markov Decision Process and an Application of Grammatical Inference
- Chapter 17Interaction Between the Spatio-Temporal Learning Rule (Non Hebbian) and Hebbian in Single Cells: A Cellular Mechanism of Reinforcement Learning
- Chapter 18Reinforcement Learning Embedded in Brains and Robots
- Chapter 19Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems
- Chapter 20Multi-Automata Learning
- Chapter 21Abstraction for Genetics-Based Reinforcement Learning
- Chapter 22Reinforcement Learning to Support Meta-Level Control in Air Traffic Management