Networks of neuron-like processing units, called connectionist systems, have interesting computational properties making them attractive for both psychological modeling and potential application in artificial intelligence. Recent research has led to some promising algorithms for learning in such networks. This project advances such research by developing a mathematically well-founded approach to the design of algorithms for the particular problem of reinforcement learning in connectionist networks of stochastic units. Particular algorithms are developed for learning problems having an important temporal component, such as control problems with feedback delays of unknown duration or problems involving recognition or production of time-varying signals. These algorithms are required to admit a suitable on-line implementation, in which the learning occurs within the operating system. In addition to making advances in the theory of such algorithms, this project involves implementing the more promising candidates and evaluating their performance in simulation experiments. Primary criteria for evaluation are learning efficiency and suspectibility to convergence to suboptimal states. In addition, this project explores the applicability of such algorithms to specific problems in artificial intelligence and robotics through the study of suitably scaled-down versions of these problems. Such problems include speech recognition and adaptive sensorimotor control.
