Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance advancements. By fusing the strengths of both approaches, ERL has emerged as a promising research direction. This survey offers a comprehensive overview of the diverse research branches in ERL. Specifically, we systematically summarize recent advancements in relevant algorithms and identify three primary research directions: EA-assisted optimization of RL, RL-assisted optimization of EA, and synergistic optimization of EA and RL. Following that, we conduct an in-depth analysis of each research direction, organizing multiple research branches. We elucidate the problems that each branch aims to tackle and how the integration of EA and RL addresses these challenges. In conclusion, we discuss potential challenges and prospective future research directions across various research directions.

翻译：进化强化学习（Evolutionary Reinforcement Learning, ERL）将进化算法（Evolutionary Algorithms, EA）与强化学习（Reinforcement Learning, RL）相结合以进行优化，已展现出显著的性能提升。通过融合两种方法的优势，ERL已发展成为一个有前景的研究方向。本综述全面概述了ERL中的不同研究分支。具体而言，我们系统地总结了相关算法的最新进展，并确定了三个主要研究方向：EA辅助的RL优化、RL辅助的EA优化，以及EA与RL的协同优化。在此基础上，我们对每个研究方向进行了深入分析，梳理了多个研究分支。我们阐明了每个分支旨在解决的问题，以及EA与RL的集成如何应对这些挑战。最后，我们讨论了不同研究方向中潜在的挑战和未来的研究展望。