Reinforcement Learning Theory and Applications 1st Edition by Cornelius Weber, Mark Elshaw, Norbert Michael Mayer ISBN 3902613149 9783902613141 by Edited By: Cornelius Weber, Mark Elshaw And Norbert Michael Mayer 978-3-902613-14-1 instant download after payment.

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ISBN 10: 3902613149

ISBN 13: 9783902613141 

Author: Cornelius Weber, Mark Elshaw, Norbert Michael Mayer

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. Two other learning paradigms exist. Supervised learning has initially been successful in prediction and classification tasks, but is not brain-like. Unsupervised learning is about understanding the world by passively mapping or clustering given data according to some order principles, and is associated with the cortex in the brain. In reinforcement learning an agent learns by trial and error to perform an action to receive a reward, thereby yielding a powerful method to develop goal-directed action strategies. It is predominately associated with the basal ganglia in the brain.
The first 11 chapters of this book, Theory, describe and extend the scope of reinforcement learning. The remaining 11 chapters, Applications, 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. We would like to thank all contributors to this book for their research and effort.

Table of contents:

1. Neural Forecasting Systems

2. Reinforcement Learning in System Identification

3. Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design

4. Superposition-Inspired Reinforcement Learning and Quantum Reinforcement Learning

5. An Extension of Finite-state Markov Decision Process and an Application of Grammatical Inference

6. Interaction Between the Spatio-Temporal Learning Rule (Non Hebbian) and Hebbian in Single Cells: A Cellular Mechanism of Reinforcement Learning

7. Reinforcement Learning Embedded in Brains and Robots

8. Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems

9. Multi-Automata Learning

10. Abstraction for Genetics-Based Reinforcement Learning

11. Dynamics of the Bush-Mosteller Learning Algorithm in 2x2 Games

12. Modular Learning Systems for Behavior Acquisition in Multi-Agent Environment

13. Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradigm

14. Water Allocation Improvement in River Basin Using Adaptive Neural Fuzzy Reinforcement Learning Approach

15. Reinforcement Learning for Building Environmental Control

16. Model-Free Learning Control of Chemical Processes

17. Reinforcement Learning-Based Supervisory Control Strategy for a Rotary Kiln Process

18. Inductive Approaches Based on Trial/Error Paradigm for Communications Network

19. The Allocation of Time and Location Information to Activity-Travel Sequence Data by Means of Reinforcement Learning

20. Application on Reinforcement Learning for Diagnosis Based on Medical Image

21. RL Based Decision Support System for u-Healthcare Environment

22. Reinforcement Learning to Support Meta-Level Control in Air Traffic Management

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Tags: Cornelius Weber, Mark Elshaw, Norbert Michael Mayer, Reinforcement, Learning, Theory, Applications