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.

進化強化學習 (ERL) 通過整合進化算法 (EAs) 和強化學習 (RL) 進行優化，展現了卓越的性能提升。本文綜述了ERL中多樣的研究分支，系統地總結了最近相關算法的發展，並確定了三個主要的研究方向：EA輔助RL優化、RL輔助EA優化以及EA和RL的協同優化。隨後，我們對每個研究方向進行了深入分析，組織了多個研究分支，闡明了每個分支旨在解決的問題以及EA和RL集成如何應對這些挑戰。最後，我們討論了不同研究方向中可能面臨的挑戰和未來的研究方向。

演化算法与强化学习的桥梁：综述