论文标题
与蒙特卡洛树搜索一起玩carcassonne
Playing Carcassonne with Monte Carlo Tree Search
论文作者
论文摘要
蒙特卡洛树搜索（MCT）是一种相对较新的抽样方法，文献中具有多种变体。它们可以应用于各种具有挑战性的领域，包括棋盘游戏，视频游戏和基于能量的问题。在这项工作中，我们探讨了在Carcassonne游戏中使用快速行动价值估算（MCTS-RAVE）的香草MCT和MCT的使用，这是一款具有欺骗性评分系统的随机游戏，进行了有限的研究。我们将基于MCT的方法的优势与Star2.5算法的优势进行了比较，此前据报道，当使用特定领域的启发式方法来评估游戏状态时，在Carcassonne游戏中产生了竞争成果。我们分析算法共享共同奖励系统时采用的策略的特殊性。基于MCT的方法始终优于STAR2.5算法，因为它们能够找到和遵循长期策略，而香草MCT的游戏表现出比MCTS-Rave更强大的游戏玩法。
Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE.
