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In recent years, the fields of mean field game (MFG) theory and machine learning (ML) have seen remarkable advancements, each transforming the landscape of their respective domains. MFG theory, initially developed to tackle optimal control problems involving large populations of interacting agents, provides a powerful framework for modeling complex systems where individual behaviors aggregate to influence the collective dynamics. On the other hand, ML, a subset of artificial intelligence, focuses on developing algorithms that enable systems to learn from data, identify patterns, and make informed decisions.

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Overview of Mean Field Theory and Machine Learning

  • 章节

  • 发表日期：

    2025年

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  • Yuhan Kang,
  • Hao Gao,
  • Zhu Han

摘要

In recent years, the fields of mean field game (MFG) theory and machine learning (ML) have seen remarkable advancements, each transforming the landscape of their respective domains. MFG theory, initially developed to tackle optimal control problems involving large populations of interacting agents, provides a powerful framework for modeling complex systems where individual behaviors aggregate to influence the collective dynamics. On the other hand, ML, a subset of artificial intelligence, focuses on developing algorithms that enable systems to learn from data, identify patterns, and make informed decisions.