Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance advancements. By fusing 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 related 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 EAs and RL addresses these challenges. In conclusion, we discuss potential challenges and prospective future research directions across various research directions. To facilitate researchers in delving into ERL, we organize the algorithms and codes involved on https://github.com/yeshenpy/Awesome-Evolutionary-Reinforcement-Learning.

翻译：进化强化学习（Evolutionary Reinforcement Learning, ERL）将进化算法（Evolutionary Algorithms, EAs）与强化学习（Reinforcement Learning, RL）相结合进行优化，已展现出显著的性能提升。通过融合这两种方法，ERL已成为一个极具前景的研究方向。本综述全面概述了ERL领域的多元研究方向。具体来说，我们系统总结了相关算法的最新进展，并识别出三大主要研究方向：EA辅助的RL优化、RL辅助的EA优化，以及EA与RL的协同优化。随后，我们对每个研究方向进行了深入分析，并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的融合如何应对这些挑战。最后，我们讨论了各研究方向中潜在的挑战及未来展望。为便于研究人员深入探索ERL，我们在https://github.com/yeshenpy/Awesome-Evolutionary-Reinforcement-Learning上整理了相关算法及代码。