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A decision heuristic for Monte Carlo tree search doppelkopf agents

机译：蒙特卡罗树搜索doppelkopf代理的决策启发式

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摘要

This work builds up on previous research by Sievers and Helmert, who developed an Monte Carlo Tree Search based doppelkopf agent. This four player card game features a larger state space than skat due to the unknown cards of the contestants. Additionally, players face the unique problem of not knowing their teammates at the start of the game. Figuring out the player parties is a key feature of this card game and demands differing play styles depending on the current knowledge of the game state. In this work we enhance the Monte Carlo Tree Search agent created by Sievers and Helmert with a decision heuristic. Our goal is to improve the quality of playouts, by suggesting high quality moves and predicting enemy moves based on a neural network classifier. This classifier is trained on an extensive history of expert player moves recorded during official doppelkopf tournaments. Different network architectures are discussed and evaluated based on their prediction accuracy. The best performing network was tested in a direct comparison with the previous Monte Carlo Tree Search agent by Sievers and Helmert. We show that high quality predictions increase the quality of playouts. Overall, our simulations show that adding the decision heuristic increased the strength of play under comparable computational effort.
机译：这项工作是建立在Sievers和Helmert先前的研究的基础上的，Sievers和Helmert开发了基于蒙特卡洛树搜索的doppelkopf代理。由于参赛者的未知纸牌，此四人纸牌游戏的状态空间比skat大。另外，玩家面临独特的问题，即在游戏开始时不认识他们的队友。弄清楚玩家聚会是该纸牌游戏的关键特征，并且根据当前游戏状态的知识要求使用不同的游戏风格。在这项工作中，我们通过决策启发式方法增强了由Sievers和Helmert创建的Monte Carlo树搜索代理。我们的目标是通过提出高质量的举动并基于神经网络分类器预测敌人的举动来提高淘汰赛的质量。该分类器接受了在官方doppelkopf锦标赛中记录的广泛的专家运动员移动历史记录。基于不同的网络体系结构的预测精度进行讨论和评估。通过与Sievers和Helmert先前的Monte Carlo Tree Search代理进行直接比较，测试了性能最佳的网络。我们表明，高质量的预测可以提高播放质量。总体而言，我们的模拟表明，在可比的计算工作量下，添加决策启发法可以提高比赛的实力。

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