{"type":"ScholarlyArticle","title":"Efficient Skill Learning Using Abstraction Selection","authors":[{"name":"George Konidaris","slug":"george-konidaris","given":"George","family":"Konidaris","affiliation":""},{"name":"Andrew Barto","slug":"andrew-barto","given":"Andrew","family":"Barto","affiliation":""}],"abstract":"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.","doi":null,"url":"https://ijcai.org/proceedings/2009/187","pdf":"https://ijcai.org/proceedings/2009/0187.pdf","markdown":"https://ijcai.org/proceedings/2009/187.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/187","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":187,"pages":{"first":null,"last":null},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}