基于强化学习的超启发学术的审查
Cuixia Li1, Xiang Wei1, Jing Wang1
1School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, Henan, China.
PeerJ. Computer science
|July 10, 2024
概括
基于强化学习的超启发式学习 (RL-HH) 通过将全球搜索与自适应式学习相结合来增强优化. 本综述介绍了一个一般的框架,对RL-HH算法进行分类,以指导未来对这种优化趋势的研究.
科学领域:
- 优化算法 优化算法
- 人工智能的人工智能
- 计算智能是一种计算智能.
背景情况:
- 基于强化学习的超启发式学习 (RL-HH) 集成了超启发式学习 (HH) 和强化学习 (RL) 进行动态策略调整.
- RL-HH在解决复杂的现实世界优化问题方面表现出有效性.
- 目前,对于新兴的RL-HH领域缺乏全面的审查和框架.
研究的目的:
- 提供对现有的基于强化学习的超启发学术进行全面的审查.
- 为理解和分类RL-HH算法提供一个一般框架.
- 确定当前的研究缺口,并概述RL-HH的未来研究方向.
主要方法:
- 对现有的基于强化学习的超启发学术进行系统的文献审查.
- 制定RL-HH的一般框架.
- 将RL-HH算法分为基于价值的和基于政策的方法.
- 每个类别内的典型算法的详细摘要和描述.
主要成果:
- 识别和分类基于强化学习的当前超启发式学习.
- 关于RL-HH的一般化框架的介绍.
- 基于价值和基于政策的RL-HH算法的详细描述.
- 分析现有的研究缺陷和未来的研究途径.
结论:
- 基于强化学习的超启发学代表了优化方面的重大进步.
- 拟议的框架有助于对RL-HH景观进行结构化的理解.
- 需要进一步的研究来解决所发现的缺陷,并探索RL-HH的新方向.
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