Abstract
In our participation to the Cross-Domain Heuristic Search Challenge (CHeSC 2011) [1] we developed an approach based on Reinforcement Learning for the automatic, on-line selection of low-level heuristics across different problem domains. We tested different memory models and learning techniques to improve the results of the algorithm. In this paper we report our design choices and a comparison of the different algorithms we developed.
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References
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Di Gaspero, L., Urli, T. (2012). Evaluation of a Family of Reinforcement Learning Cross-Domain Optimization Heuristics. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_32
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DOI: https://doi.org/10.1007/978-3-642-34413-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34412-1
Online ISBN: 978-3-642-34413-8
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