基于策略的强化学习在人类行为建模中的分类匹配

O. Deng, Q. Jin
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引用次数: 1

摘要

本文探讨了虚拟网络社区中的人类行为,特别是个人或群体对内部和外部刺激的反应潜力和表达能力,并以分类匹配为典型例子。提出了一种基于多智能体强化学习(MARL)的建模方法,在A3C算法中加入多头注意函数,提高学习效率。该方法通过各种环境参数设置和代理动作策略来模拟特定场景下的人类行为。在我们的实验中,我们使用强化学习来服务特定的代理,这些代理从环境状态和竞争对手行为中学习,优化策略以获得更好的结果。仿真包括个体和群体两个层面,展示了形成竞争优势的可能路径。这种建模方法为进一步分析各种社会经济问题中人类行为、社区和组织的进化动态提供了一种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling
This paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approach based on Multi-Agent Reinforcement Learning (MARL) is proposed, adding a multi-head attention function to the A3C algorithm to enhance learning effectiveness. This approach simulates human behavior in certain scenarios through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning is employed to serve specific agents that learn from environment status and competitor behaviors, optimizing strategies to achieve better results. The simulation includes individual and group levels, displaying possible paths to forming competitive advantages. This modeling approach provides a means for further analysis of the evolutionary dynamics of human behavior, communities, and organizations in various socioeconomic issues.
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