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<p>Optimization problems in the real world usually involve multiple potentially conflicting objectives, and although traditional multi-objective optimization algorithms can solve various optimization problems better, they may not perform well when facing complex, dynamic, and high-dimensional optimization problems. In order to make up for the shortcomings of traditional algorithms in adapting to complex real-world scenarios, in recent years, researchers have conducted a lot of research on the combination of multi-objective optimization algorithms and reinforcement learning. In this paper, we provide a comprehensive review of recent research efforts. We classify current multi-objective optimization algorithms into two categories: mainstream multi-objective evolutionary algorithms and other typical algorithms, describe the types of problems to which each algorithm is applicable and the algorithmic improvements made by recent work, and systematically outline how different types of algorithms can be combined with reinforcement learning. Finally, we discuss some remaining challenges and suggest several promising directions for future research.</p>

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Reinforcement Learning for Multi-Objective Optimization: A Review

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Kaisheng He,
  • Chengbin Chen,
  • Sifan Chen,
  • Baihe Chen,
  • Ang Zhang,
  • Pingping Chen,
  • Zhensheng Wang,
  • Zhiqiang Wu

摘要

Optimization problems in the real world usually involve multiple potentially conflicting objectives, and although traditional multi-objective optimization algorithms can solve various optimization problems better, they may not perform well when facing complex, dynamic, and high-dimensional optimization problems. In order to make up for the shortcomings of traditional algorithms in adapting to complex real-world scenarios, in recent years, researchers have conducted a lot of research on the combination of multi-objective optimization algorithms and reinforcement learning. In this paper, we provide a comprehensive review of recent research efforts. We classify current multi-objective optimization algorithms into two categories: mainstream multi-objective evolutionary algorithms and other typical algorithms, describe the types of problems to which each algorithm is applicable and the algorithmic improvements made by recent work, and systematically outline how different types of algorithms can be combined with reinforcement learning. Finally, we discuss some remaining challenges and suggest several promising directions for future research.