Efficient Skill Learning using Abstraction Selection

George Dimitri Konidaris, Andrew G. Barto
2009 International Joint Conference on Artificial Intelligence  
We present an algorithm for selecting an appropriate abstraction when learning a new skill. We show empirically that it can consistently select an appropriate abstraction using very little sample data, and that it significantly improves skill learning performance in a reasonably large real-valued reinforcement learning domain.
dblp:conf/ijcai/KonidarisB09 fatcat:bphg3b2igrgtjc6wciit23u3ki