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Reinforcement Learning of Strategies for Settlers of Catan AbstractIn this paper we study the application of machine learning methods in complex computer games. A combination of hierarchical reinforcement learning and simple heuristics is used to learn strategies for the game Settlers of Catan (© 1995 by Kosmos Verlag, Stuttgart) via self-play. Since existing algorithms for function approximation are not well-suited for problems of this size and complexity, we present a novel use of model trees for state-action value prediction in a sophisticated computer game. Furthermore we demonstrate how apriori knowledge about the game can reduce the learning time and improve the performance of learning virtual agents. We compare several different learning approaches, and it turns out that, despite the simplicity of the architecture, a combination of learning and builtin knowledge yields strategies that are able to challenge and even beat human players in a complex game like this.
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