Effective Control Knowledge Transfer Through Learning Skill and Representation Hierarchies

Asadi, Mehran
Effective Control Knowledge Transfer Through Learning Skill and Representation Hierarchies
International Joint Conference on Artificial Intelligence 2007 (pdf - 192 KB)

Abstract: Learning capabilities of computer systems still lag far behind biological systems. One of the reasons can be seen in the ineficient reuse of control knowledge acquired over the lifetime of the artificial learning system. To address this de iciency, this paper presents a learning architecture which transfers control knowledge in the form of behavioral skills and corresponding representation concepts from one task to subsequent learning tasks. The presented system uses this knowledge to construct a more compact state space representation for learning while assuring bounded optimality of the learned task policy by utilizing a representation hierarchy. Experimental results show that the presented method can signiicantly outperform learning on a flat state space representation and the MAXQ method for hierarchical reinforcement learning.