Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey
CoRR(2024)
摘要
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.
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