Combining Monte Carlo tree search and apprenticeship learning for capture the flag

IEEE Conference on Computational Intelligence and Games, pp. 154.0-161.0, 2015.

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In this paper we introduce a novel approach to agent control in competitive video games which combines Monte Carlo Tree Search (MCTS) and Apprenticeship Learning (AL). More specifically, an opponent model created through AL is used during the expansion phase of the Upper Confidence Bounds for Trees (UCT) variant of MCTS. We show how this ...More



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