Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Solving factored MDPs using non-homogeneous partitions
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Behavior transfer for value-function-based reinforcement learning
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Concurrent hierarchical reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning to generalize and reuse skills using approximate partial policy homomorphisms
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Activity knowledge transfer in smart environments
Pervasive and Mobile Computing
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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.