8 Reinforcement-Learning Control and Pattern Recognition Systems

This chapter presents the formulation of the learning models and its application to the synthesis of control systems and pattern-recognition systems in such a manner that these systems may be said to possess attributes of learning.

Abstract

This chapter presents the formulation of the learning models and its application to the synthesis of control systems and pattern-recognition systems in such a manner that these systems may be said to possess attributes of learning. Learning may be defined as the process by which an activity originates or is changed through reaction to an encountered situation which is not due to “native” response tendencies, maturation, or temporary states of the organism. The chapter also discusses the basic properties of reinforcement-learning pattern recognition systems. There are two types of control systems—open-loop and closed-loop. Just as for learning control systems, correspondences exist between elements of stochastic learning theory and pattern recognition systems. Mathematical learning models combine the mathematical properties and psychological concepts of learning. They are formulated from experimental results and attempt to predict learning behavior quantitatively. One of the most important principles in all learning theory is the law of reinforcement.