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Updated: Jan 11, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

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一个统一和高效的培训框架,用于开放式的非过渡性游戏

Shaokang Dong1, Chao Li2, Shangdong Yang2

  • 1School of Computer and Electronic Information, Nanjing Normal University, Nanjing, China; State Key Laboratory for Novel Software Technology, Nanjing University, China.

Neural networks : the official journal of the International Neural Network Society
|November 15, 2025
PubMed
概括

本研究介绍了一种统一和有效的游戏 (UEP) 框架,用于解决复杂游戏中的纳什平衡 (NE). UEP结合了自我游戏 (SP) 和政策空间响应预言 (PSRO) 以实现更快,更强大的政策生成.

关键词:
纳什平衡中的纳什平衡开放式的非过渡性的游戏.政策空间的响应预言.自己玩的游戏.

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