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arXiv 2501.09330cs.GT
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利用玩家-策略网络求解无限玩家博弈

Solving Infinite-Player Games with Player-to-Strategy Networks

Carlos Martin, Tuomas Sandholm

AI总结:

针对无限玩家博弈求解难题,提出玩家-策略网络(P2SN)表示无限玩家策略剖面,结合共享参数同步梯度(SPSG)算法训练以寻找近似纳什均衡,可适配多类复杂博弈场景。

摘要:

我们提出了一种求解具有可数或不可数无限数量玩家的博弈的新方法。这类博弈常被用于建模包含大量智能体的多智能体系统,而这类系统在经济学、金融市场、人群动力学、拥堵分析、流行病学以及种群生态学等诸多领域中十分常见。我们的两项主要贡献如下:第一,我们提出了一种表示无限数量玩家的策略剖面的方法,将其命名为Player-to-Strategy Network(玩家-策略网络,简称P2SN)。该网络将玩家映射到策略,并利用神经网络的泛化能力同时在无限数量的输入(玩家)上进行学习。第二,我们提出了一种名为Shared-Parameter Simultaneous Gradient(共享参数同步梯度,简称SPSG)的算法用于训练该网络,目标是找到近似纳什均衡。该算法推广了同步梯度上升及其变体——后者是多智能体强化学习中经典的均衡求解动力学方法。我们在无限玩家博弈上测试了我们的方法,观察到它能够收敛到近似纳什均衡。我们的方法可以处理具有无限状态、无限玩家、无限动作(以及基于这些动作的混合策略)以及不连续效用函数的博弈。

英文摘要:

We present a new approach to solving games with a countably or uncountably infinite number of players. Such games are often used to model multiagent systems with a large number of agents. The latter are frequently encountered in economics, financial markets, crowd dynamics, congestion analysis, epidemiology, and population ecology, among other fields. Our two primary contributions are as follows. First, we present a way to represent strategy profiles for an infinite number of players, which we name a Player-to-Strategy Network (P2SN). Such a network maps players to strategies, and exploits the generalization capabilities of neural networks to learn across an infinite number of inputs (players) simultaneously. Second, we present an algorithm, which we name Shared-Parameter Simultaneous Gradient (SPSG), for training such a network, with the goal of finding an approximate Nash equilibrium. This algorithm generalizes simultaneous gradient ascent and its variants, which are classical equilibrium-seeking dynamics used for multiagent reinforcement learning. We test our approach on infinite-player games and observe its convergence to approximate Nash equilibria. Our method can handle games with infinitely many states, infinitely many players, infinitely many actions (and mixed strategies on them), and discontinuous utility functions.

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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