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Evolutionary Reinforcement Learning: A Systematic Review and Future Directions.

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Titel: Evolutionary Reinforcement Learning: A Systematic Review and Future Directions.
Autoren: Lin, Yuanguo, Lin, Fan, Cai, Guorong, Chen, Hong, Zou, Linxin, Liu, Yunxuan, Wu, Pengcheng
Quelle: Mathematics (2227-7390); Mar2025, Vol. 13 Issue 5, p833, 33p
Schlagwörter: MACHINE learning, ARTIFICIAL intelligence, DEEP learning, EVOLUTIONARY algorithms, RESEARCH personnel
Abstract: In response to the limitations of reinforcement learning and Evolutionary Algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. This systematic review aims to provide a comprehensive analysis of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms and identifying critical gaps in relevant application tasks. The review begins by outlining the technological foundations of EvoRL, detailing the complementary relationship between EAs and reinforcement learning algorithms to address the limitations of reinforcement learning, such as parameter sensitivity, sparse rewards, and its susceptibility to local optima. We then delve into the challenges faced by both reinforcement learning and EvoRL, exploring the utility and limitations of EAs in EvoRL. EvoRL itself is constrained by the sampling efficiency and algorithmic complexity, which affect its application in areas like robotic control and large-scale industrial settings. Furthermore, we address significant open issues in the field, such as adversarial robustness, fairness, and ethical considerations. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation, self-improvement, scalability, interpretability, and so on. To quantify the current state, we analyzed about 100 EvoRL studies, categorizing them based on algorithms, performance metrics, and benchmark tasks. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence. [ABSTRACT FROM AUTHOR]
Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: In response to the limitations of reinforcement learning and Evolutionary Algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. This systematic review aims to provide a comprehensive analysis of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms and identifying critical gaps in relevant application tasks. The review begins by outlining the technological foundations of EvoRL, detailing the complementary relationship between EAs and reinforcement learning algorithms to address the limitations of reinforcement learning, such as parameter sensitivity, sparse rewards, and its susceptibility to local optima. We then delve into the challenges faced by both reinforcement learning and EvoRL, exploring the utility and limitations of EAs in EvoRL. EvoRL itself is constrained by the sampling efficiency and algorithmic complexity, which affect its application in areas like robotic control and large-scale industrial settings. Furthermore, we address significant open issues in the field, such as adversarial robustness, fairness, and ethical considerations. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation, self-improvement, scalability, interpretability, and so on. To quantify the current state, we analyzed about 100 EvoRL studies, categorizing them based on algorithms, performance metrics, and benchmark tasks. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/math13050833
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      – Code: eng
        Text: English
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        PageCount: 33
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        Type: general
      – SubjectFull: ARTIFICIAL intelligence
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      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: EVOLUTIONARY algorithms
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      – SubjectFull: RESEARCH personnel
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      – TitleFull: Evolutionary Reinforcement Learning: A Systematic Review and Future Directions.
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              M: 03
              Text: Mar2025
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              Y: 2025
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