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新算法解决具有连续动作空间的通用和博弈

研究人员开发了用于近似具有连续动作空间的通用和博弈的新型分布式算法。这些算法解决了当玩家只能访问自己的效用函数时计算最紧密的 \(\alpha\)-potential 近似值的挑战。所提出的方法涉及有限元组重构,并且可以与对偶内预言机或投影零阶外方法相结合,从而在准确性和计算时间之间进行权衡。 AI

影响 这项研究可能导致在复杂、连续环境中进行多智能体决策的更有效算法。

排序理由 该条目是一篇在 arXiv 上发表的研究论文,详细介绍了多智能体系统特定问题的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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新算法解决具有连续动作空间的通用和博弈

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该条目是一篇在 arXiv 上发表的研究论文,详细介绍了多智能体系统特定问题的新算法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Chinmay Maheshwari ·

    分布式算法用于一般和博弈中的 $α$-势函数

    We study the problem of computing the tightest \(α\)-potential approximation of a general-sum game over continuous action spaces, within a prescribed class of potential functions and when each player has access only to its own utility function. The difficulty is twofold: the appr…