English(EN) Expected flow networks in stochastic environments and two-player zero-sum games

新型流网络应对随机和对抗性AI挑战

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出了期望流网络（EFlowNets），这是生成流网络（GFlowNets）的改进版本，旨在在随机环境中有效运行。与现有的GFlowNet模型相比，EFlowNets在蛋白质设计等任务中表现出更优越的性能。在此基础上，研究人员开发了用于双人零和博弈的对抗流网络（AFlowNets），结果表明AFlowNets通过自我对弈可以识别出Connect-4中超过80%的最优走法，并在竞技比赛中超越AlphaZero。 AI

影响引入了在随机和对抗性环境中运行AI的新方法，有望提高科学发现和游戏AI等领域的性能。

排序理由该集群包含一篇研究论文，介绍了新颖的AI模型（EFlowNets和AFlowNets）及其在科学发现和游戏中的应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Marco Jiralerspong, Bilun Sun, Danilo Vucetic, Tianyu Zhang, Yoshua Bengio, Gauthier Gidel, Esmeralda S. Whitammer · 2026-09-01 04:00

随机环境中的期望流网络与双人零和博弈

arXiv:2310.02779v3 Announce Type: replace Abstract: Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied to various structured object generation tasks, sampling a diverse set of high-re…

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报道来源 [1]

随机环境中的期望流网络与双人零和博弈

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