{"type":"ScholarlyArticle","title":"State Similarity Based Approach for Improving Performance in RL","authors":[{"name":"Sertan Girgin","slug":"sertan-girgin","given":"Sertan","family":"Girgin","affiliation":""},{"name":"Faruk Polat","slug":"faruk-polat","given":"Faruk","family":"Polat","affiliation":""},{"name":"Reda Alhajj","slug":"reda-alhajj","given":"Reda","family":"Alhajj","affiliation":""}],"abstract":"This paper employs state similarity to improve reinforcement learning performance. This is achieved by first identifying states with similar sub-policies. Then, a tree is constructed to be used for locating common action sequences of states as derived from possible optimal policies. Such sequences are utilized for defining a similarity function between states, which is essential for reflecting updates on the action-value function of a state onto all similar states. As a result, the experience acquired during learning can be applied to a broader context. Effectiveness of the method is demonstrated empirically.","doi":null,"url":"https://ijcai.org/proceedings/2007/131","pdf":"https://ijcai.org/proceedings/2007/0131.pdf","markdown":"https://ijcai.org/proceedings/2007/131.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/131","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":131,"pages":{"first":817,"last":821},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}