{"type":"ScholarlyArticle","title":"Towards More Practical Reinforcement Learning","authors":[{"name":"Travis Mandel","slug":"travis-mandel","given":"Travis","family":"Mandel","affiliation":""}],"abstract":"Reinforcement Learning is beginning to be applied outside traditional domains such as robotics, and into human-centric domains such as healthcare and education. In these domains, two problems are critical to address: We must be able to evaluate algorithms with a collection of prior data if one is available, and we must devise algorithms that carefully trade off exploration and exploitation in such a way that they are guaranteed to converge to optimal behavior quickly, while retaining very good performance with limited data. In this thesis, I examine these two problems, with an eye towards applications to educational games.","doi":null,"url":"https://ijcai.org/proceedings/2015/638","pdf":"https://ijcai.org/proceedings/2015/0638.pdf","markdown":"https://ijcai.org/proceedings/2015/638.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/638","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":638,"pages":{"first":4381,"last":4381},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}