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arXiv 2602.01944math.PR
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一类马尔可夫零和停止博弈的前向算法

A forward algorithm for a class of Markov zero-sum stopping games

Toulouse School of Economics(图卢兹经济学院);Institut de Mathématiques de Toulouse(图卢兹数学研究所);University of Toulouse(图卢兹大学)

Nhat-Thang Le

AI总结:

本文提出了一种高效算法,用于计算有限状态空间中马尔可夫零和停止博弈的价值函数,并讨论了迭代次数及计算实现。

摘要:

在本文中,我们提出了一种新的高效算法,用于计算具有对立利益的两个玩家特征的零和停止博弈的价值函数。这可以看作是(单人)最优停止问题的'前向算法'的游戏版本,最初由Irle [6] 为离散时间马尔可夫过程引入,后来由Miclo & Villeneuve [8] 重新审视用于连续时间马尔可夫过程在一般状态空间上。本文聚焦于由同质马尔可夫过程驱动的有限状态空间博弈,并讨论所需的迭代次数。还提供了几个特定例子的计算实现示例。

英文摘要:

In this paper, we propose a new efficient algorithm to compute the value function for zero-sum stopping games featuring two players with opposing interests. This can be seen as a game version of the ''forward algorithm'' for (one-player) optimal stopping problem, first introduced by Irle [6] for discrete-time Markov processes and later revisited by Miclo \& Villeneuve [8] for continuous-time Markov processes on general state spaces. This paper focuses on a game driven by a homogeneous Markov process taking values in a finite state space and also discusses about the number of iterations needed. Illustrated computational implementations for a few particular examples are also provided.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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