Reinforcement Learning

Reinforcement Learning

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

  • Neural Forecasting Systems
  • Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradigm
  • Water Allocation Improvement in River Basin Using Adaptive Neural Fuzzy Reinforcement Learning Approach
  • Reinforcement Learning for Building Environmental Control
  • Model-Free Learning Control of Chemical Processes
  • Reinforcement Learning-Based Supervisory Control Strategy for a Rotary Kiln Process
  • Inductive Approaches Based on Trial/Error Paradigm for Communications Network
  • The Allocation of Time and Location Information to Activity-Travel Sequence Data by Means of Reinforcement Learning
  • Application on Reinforcement Learning for Diagnosis Based on Medical Image
  • RL Based Decision Support System for u-Healthcare Environment
  • Modular Learning Systems for Behavior Acquisition in Multi-Agent Environment
  • Dynamics of the Bush-Mosteller Learning Algorithm in 2×2 Games
  • Reinforcement Learning in System Identification
  • Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design
  • Superposition-Inspired Reinforcement Learning and Quantum Reinforcement Learning
  • An Extension of Finite-state Markov Decision Process and an Application of Grammatical Inference
  • Interaction Between the Spatio-Temporal Learning Rule (Non Hebbian) and Hebbian in Single Cells: A Cellular Mechanism of Reinforcement Learning
  • Reinforcement Learning Embedded in Brains and Robots
  • Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems
  • Multi-Automata Learning
  • Abstraction for Genetics-Based Reinforcement Learning
  • Reinforcement Learning to Support Meta-Level Control in Air Traffic Management

Book Details

Author(s): Cornelius Weber, Mark Elshaw and Norbert Michael Mayer
Format(s): PDF
File size: 11.72 MB
Number of pages: 424
Link: Download or read online.

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