Hierarchical reinforcement learning and decision making for intelligent machines

Abstract

A methodology for performance improvement of intelligent machines based on hierarchical reinforcement learning is introduced. Machine decision making and learning are based on a cost function which includes reliability and a computational cost of algorithms at the three levels of the hierarchy proposed by Saridis. Despite this particular formalization, the methodology intends to be sufficiently general to encompass different types of architectures and applications. Novel contributions of this work include the definition of a cost function combining reliability and complexity, recursively improved through feedback, a hierarchical reinforcement learning and decision making algorithm which uses that cost function, and a methodology supported on information-based complexity for joint measure of algorithm cost and reliability. Results of simulations show the application of the formalism to intelligent robotic systems. >