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Română(RO) Real-Time Parallel Counterfactual Regret Minimization

新论文详述在线优化和博弈的更快速遗憾最小化

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的arXiv论文探讨了在线优化和博弈论中遗憾最小化的进展。第一篇论文介绍了一种更简单、计算效率更高的算法，用于最小化线性交换遗憾，并具有接近最优的界限，该算法利用了基于响应的可接近性。第二篇论文提出了Parallel CFR，一个用于实时、深度受限反事实遗憾最小化的框架，通过并行化迭代和将叶节点评估卸载到GPU来显著加速。 AI

影响 这些论文推进了遗憾最小化的理论和实践方法，这对于在复杂决策环境中开发更强大、更高效的AI代理至关重要。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了遗憾最小化的新算法和框架。

在 arXiv cs.AI 阅读 →

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新论文详述在线优化和博弈的更快速遗憾最小化

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两篇在arXiv上发表的学术论文，详细介绍了遗憾最小化的新算法和框架。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ioannis Anagnostides, Gabriele Farina, Maxwell Fishelson, Haipeng Luo, Jon Schneider ·

    通过基于响应的可接近性最小化遗憾

    arXiv:2602.06264v3 Announce Type: replace Abstract: We consider the problem of minimizing different notions of swap regret in online optimization. These forms of regret are tightly connected to correlated equilibrium concepts in games, and have been more recently shown to guarant…

  2. arXiv cs.AI TIER_1 Română(RO) · Longbo Huang ·

    实时并行反事实遗憾最小化

    Counterfactual Regret Minimization (CFR) is the dominant algorithmic family for solving large imperfect-information games, underpinning breakthroughs such as Libratus and Pluribus in No-Limit Texas Hold'em poker. In real-time game-playing systems, the solver must compute a near-e…

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