Evolutionary Reinforcement Learning: A Systematic Review and Future Directions

Yuanguo Lin, Fan Lin, Guorong Cai, Hong Chen (), Linxin Zou, Yunxuan Liu and Pengcheng Wu
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Yuanguo Lin: School of Computer Engineering, Jimei University, Xiamen 361021, China
Fan Lin: School of Informatics, Xiamen University, Xiamen 361005, China
Guorong Cai: School of Computer Engineering, Jimei University, Xiamen 361021, China
Hong Chen: Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China
Linxin Zou: School of Cyber Science and Engineering, Wuhan University, Wuhan 430074, China
Yunxuan Liu: School of Computer Engineering, Jimei University, Xiamen 361021, China
Pengcheng Wu: Webank-NTU Joint Research Institute on Fintech, Nanyang Technological University, Singapore 639798, Singapore

Mathematics, 2025, vol. 13, issue 5, 1-33

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.

Keywords: evolutionary reinforcement learning; evolutionary algorithms; deep learning; policy search; evolution strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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