Learning By Doing; Synthesis and Refinement of Control Strategy

This work describes an application of machine learning to the synthesis and refinement of control strategy. Given a model of the target system´s dynamic behavior, we describe a program that proposes possible control strategies and tests their behavior by exploring a curriculum of training problems. By generalizng from its successes and failures, the program learns to concentrate its efforts on control structures which are most likely to reward study.