Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms

TL;DR

This survey comprehensively reviews the integration of Evolutionary Algorithms and Reinforcement Learning, highlighting recent advancements, research directions, and future challenges in hybrid ERL algorithms.

Contribution

It systematically categorizes ERL research, summarizes recent algorithmic developments, and discusses future research challenges and directions.

Findings

Identified three primary research directions in ERL

Summarized recent algorithmic advancements in ERL

Discussed future challenges and research opportunities

Abstract

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.