  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.
