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Updated: Sep 16, 2025

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在可计数的随机双人游戏中可达到的战略复杂性
Stefan Kiefer1, Richard Mayr2, Mahsa Shirmohammadi3
1University of Oxford, Oxford, UK.
概括
在无限随机游戏中最大化玩家可能需要无限的内存,而最小化玩家有无限的动作. 统一的策略,即使有有限的行动,也可能不是没有记忆的.
科学领域:
- 理论计算机科学 理论计算机科学
- 游戏理论 游戏理论
- 随机过程 随机过程
背景情况:
- 在不确定性下做决策时,具有可达性目标的随机2人游戏是基本的.
- 了解策略内存需求对于计算复杂性和算法设计至关重要.
研究的目的:
- 在无限随机游戏中充分描述epsilon-optimal和最佳策略的内存需求.
- 调查动作集大小和一致性对内存要求的影响.
主要方法:
- 对epsilon-optimal和最佳策略的记忆界限的分析.
- 考虑统一的策略 (独立于起始状态).
- 检查特定的案例,比如无限分支的循环游戏.
主要成果:
- 如果最小化器有无限的动作集,则Epsilon-optimal最大化器策略需要无限的内存.
- 即使有保证的胜利策略,有限的内存 (步数加私人内存) 在一些无限游戏中是不够的.
- 没有内存的统一的epsilon-optimal最大化器策略可能不存在,即使是有限的动作集或有限的分支游戏.
结论:
- 无限随机游戏中的策略的内存复杂性对玩家动作集和统一性要求高度敏感.
- 在具有有限动作集的游戏中,单个公共内存位足以实现统一的epsilon-optimal Maximizer策略.
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