Advances in reinforcement learning and their implications for intelligent control

Abstract

The focus of this work is on control architectures that are based on reinforcement learning. A number of recent advances that have contributed to the viability of reinforcement learning approaches to intelligent control are surveyed. These advances include the formalization of the relationship between reinforcement learning and dynamic programming, the use of internal predictive models to improve learning rate, and the integration of reinforcement learning with active perception. On the basis of these advances and other results, it is concluded that control architectures base on reinforcement learning are now in a position to satisfy many of the criteria associated with intelligent control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>