State Similarity Based Approach for Improving Performance in RL
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 817-821.
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.
@inproceedings{ijcai2007p131,
title = {State Similarity Based Approach for Improving Performance in RL},
author = {Girgin, Sertan and Polat, Faruk and Alhajj, Reda},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {817--821},
year = {2007},
url = {https://ijcai.org/proceedings/2007/131},
}
TY - CPAPER TI - State Similarity Based Approach for Improving Performance in RL AU - Girgin, Sertan AU - Polat, Faruk AU - Alhajj, Reda T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 817 EP - 821 UR - https://ijcai.org/proceedings/2007/131 ER -Copied
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