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

• URL: http://arxiv.org/abs/2401.11963v4
• Date: Sat, 22 Jun 2024 03:56:22 GMT
• Title: Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms
• Authors: Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zheng, Ke Tang,
• Abstract summary: Evolutionary Reinforcement Learning (ERL) integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization. This survey offers a comprehensive overview of the diverse research branches in ERL.
• Score: 50.91348344666895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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