Using computer poker as a testbed for solving multiagent decision problems

使用计算机扑克作为解决多智能体决策问题的测试平台

• 批准号: 8191-2011
• 负责人: Szafron, Duane
• 金额: $ 2.11万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=544492
• 关键词: Using; computer; poker; testbed; solving

Games are used to advance multi-agent decision-making techniques. Many games are deterministic (no chance) with perfect information (all info visible to all agents) like Chess and Checkers. However, many real-world scenarios with competing agents are non-deterministic with imperfect information. For two-player zero-sum perfect recall games, a recent technique called Counterfactual Regret Minimization (CFR) computes strategies that are provably convergent to an epsilon-Nash equilibrium. A Nash equilibrium strategy is useful in two-player games since it maximizes its utility against a worst-case opponent.

游戏用于推进多智能体决策技术。许多游戏都是确定性的（没有机会），具有完美的信息（所有信息对所有代理可见），例如国际象棋和西洋跳棋。然而，许多具有竞争代理的现实场景是不确定的，信息不完善。对于两人零和完美回忆游戏，一种名为反事实遗憾最小化 (CFR) 的最新技术计算出可证明收敛于 epsilon-Nash 均衡的策略。纳什均衡策略在两人博弈中非常有用，因为它可以在对抗最坏情况的对手时最大化其效用。