Abstract
In UCT algorithm, a large number of Monte-Carlo simulations are performed and their rewards are averaged to evaluate a specified action. In this paper, we propose a general approach to enhance the UCT algorithm with knowledge-based neural controllers by adjusting the probability distribution of UCT simulations. Experimental results on Dead End, the classical predator/prey game, show that our approach improves the performance of UCT significantly.
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Xie, F., Liu, Z., Wang, Y., Huang, W., Wang, S. (2010). Systematic Improvement of Monte-Carlo Tree Search with Self-generated Neural-Networks Controllers. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_25
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DOI: https://doi.org/10.1007/978-3-642-13800-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13799-0
Online ISBN: 978-3-642-13800-3
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