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

2024-01-22 · Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zheng, Ke Tang

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.

📄 PDF Abstract BibTeX arXiv:2401.11963

Code (1)

yeshenpy/awesome-evolutionary-reinforcement-learning 공식 구현

Tasks

Evolutionary Algorithmsreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecovera…

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The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates. While manual prompt engine…

cross-modal alignment Prompt Engineering Survey

Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey

2024-07-25 · Yelleti Vivek, Vadlamani Ravi, P. Radha Krishna

The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits …

Diversity Evolutionary Algorithms

Evolutionary Reinforcement Learning: A Survey

2023-03-07 · Hui Bai, Ran Cheng, Yaochu Jin

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impre…

Board Games Hyperparameter Optimization Multi-agent Reinforcement Learning reinforcement-learning+3