Abstract
Catan is a popular multiplayer board game that involves multiple gameplay notions: stochastic elements related to the dice rolls as well as to the initial placement of resources on the map and the drawing of development cards, strategic notions for the placement of the cities and the roads which call upon topological and shape recognition notions and notions of expectation of gains linked to the probabilities of the rolls of the dice. In this paper, we develop a policy for this game using a convolutional neural network. The used deep reinforcement learning algorithm is Expert Iteration [2] which has already given excellent results for Alpha Zero and its descendants.
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Acknowledgment
This work was supported in part by the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute).
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Driss, B., Cazenave, T. (2022). Deep Catan. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_32
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