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IJCAI
2007

Effective Control Knowledge Transfer through Learning Skill and Representation Hierarchies

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Effective Control Knowledge Transfer through Learning Skill and Representation Hierarchies
Learning capabilities of computer systems still lag far behind biological systems. One of the reasons can be seen in the inefficient re-use of control knowledge acquired over the lifetime of the artificial learning system. To address this deficiency, 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 significantly outperform learning on a flat state space representation and the MAXQ method for hierarchical reinforcement learning.
Mehran Asadi, Manfred Huber
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Mehran Asadi, Manfred Huber
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