Akademik Veri Yönetim Sistemi
20th International Joint Conference on Artificial Intelligence, Hyderabad, Pakistan, 6 - 12 Ocak 2007, ss.817-822
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.