Speeding-up reinforcement learning with multi-step actions

Abstract

In recent years hierarchical concepts of temporal abstraction have been integrated in the reinforcement learning framework to improve scalability. However, existing approaches are limited to domains where a decomposition into subtasks is known a priori. In this paper we propose the concept of explicitly selecting time scale related actions if no subgoalrelated abstract actions are available. This is realised with multi-step actions on different time scales that are combined in one single action set. The special structure of the action set is exploited in the MSAQ-learning algorithm. By learning on different explicitly specified time scales simultaneously, a considerable improvement of learning speed can be achieved. This is demonstrated on two benchmark problems. © Springer-Verlag Berlin Heidelberg 2002.