DIRECT ASSOCIATIVE REINFORCEMENT LEARNING-METHODS FOR DYNAMIC-SYSTEMSCONTROL

Most problems in learning to control dynamic systems involve learning under uncertainty, noise, and the lack of explicit instructional infor mation about how to perform a task. Under these circumstances, techniq ues developed by artificial intelligence researchers for 'learning fro m examples,' including the 'supervised learning' techniques studied by neural network researchers, are impractical because of the difficulty of obtaining training information (the 'examples') in the form of sit uation-action training pairs. A useful alternative in such situations is a learning technique that can discover appropriate actions in vario us situations through a search process that is guided by evaluative pe rformance feedback. Reinforcement learning methods developed by neural network researchers are examples of such techniques. This paper focus es on direct reinforcement learning techniques and discusses their rol e in learning control by relating them to similar adaptive control met hods. Several examples are also presented to illustrate the power and utility of direct reinforcement learning techniques for learning contr ol.