Persistent URL of this record https://hdl.handle.net/1887/85506
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- BNAIC_1021_2019_138
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Assessing the Potential of Classical Q-learning in General Game Playing
ϵ-greedy strategy, we propose a first enhancement, the dynamic ϵ" role="presentation" style="display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">ϵ algorithm. In addition, inspired by (Gelly & Silver, ICML 2007) we combine online search (Monte Carlo Search) to enhance offline learning, and propose QM-learning for GGP. Both enhancements improve the performance of classical Q-learning. In this work, GGP allows us to show, if augmented by appropriate enhancements, that classical table-based Q-learning can perform well in small games.Show less
- All authors
- Wang, H.; Emmerich, M.T.M.; Plaat, A.
- Editor(s)
- Atzmueller M., Duivesteijn W.
- Date
- 2019-09-25
- Title of host publication
- Artificial Intelligence
- Pages
- 138 - 150
- ISBN (print)
- 9783030319779
- ISBN (electronic)
- 9783030319786
Publication Series
- Name
- volume 1021
Conference
- Conference
- 30th Benelux Conference on Artificial Intelligence (BNAIC)
- Date
- 2018-11-08 - 2018-11-09
- Location
- Den Bosch, Netherlands