蒙特卡洛树搜索或其他算法用于随机纸牌游戏? [英] Monte Carlo Tree Search or other algorithms for a stochastic card game?

问题描述

我目前正在研究2人两人的纸牌游戏，类似于66或Schnapsen.基本上，您需要通过赢得技巧来收集积分，并且在游戏中有纸牌时，两名玩家在每轮之后都抓一张纸牌.

I am currently working on an implementation of a 2 player trick-and-draw card game, similar to 66 or Schnapsen. Basically you need to gather points by winning tricks and while there are cards in the pack, both players draw a card after each round.

我正在为游戏编写一个好的AI，它不会作弊，但实际上只使用给定游戏状态下的信息来计算最佳动作. 我一直在决定哪种算法或逻辑最适合使用. 我决定不使用Alpha-Beta修剪之类的算法，因为存在太多隐藏信息，尤其是在游戏开始时. 我阅读了许多有关蒙特卡洛树搜索"和相关UCT搜索的有趣信息，但是由于该游戏具有随机性，因此需要搜索的树将在短时间内变得巨大.

I am at the point of programming a good AI for the game that does not cheat, but really calculates the best moves by using only the information it has at the given game state. I am stuck deciding which algorithm or logic would be the best to use. I decided against algorithms like Alpha-Beta pruning because there are too much hidden information especially at the beginning of the game. I read many interesting things about the Monte Carlo Tree Search and the related UCT search, but because the game has stochastic elements, the tree needed to be searched would grow huge in a short time.

哪种算法或方法最适合使用?

Which algorithm or approach would be the best to use?

推荐答案

Here's a link to an application of UCT to Klondike Solitaire. MCTS is a perfect fit for the problem since it can deal well with the stochasticity.

您可以查看本文中介绍的稀疏方法，以找到一种限制树的宽度的方法.

You can look at the sparse method described inside the paper for a way to limit the width of the tree.

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