A review of reinforcement learning based hyper-heuristics

Author:

Li Cuixia1,Wei Xiang1,Wang Jing1,Wang Shuozhe1,Zhang Shuyan1

Affiliation:

1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, Henan, China

Abstract

The reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the learning ability of reinforcement learning (RL). This synergy allows the agent to dynamically adjust its own strategy, leading to a gradual optimization of the solution. Existing researches have shown the effectiveness of RL-HH in solving complex real-world problems. However, a comprehensive introduction and summary of the RL-HH field is still blank. This research reviews currently existing RL-HHs and presents a general framework for RL-HHs. This article categorizes the type of algorithms into two categories: value-based reinforcement learning hyper-heuristics and policy-based reinforcement learning hyper-heuristics. Typical algorithms in each category are summarized and described in detail. Finally, the shortcomings in existing researches on RL-HH and future research directions are discussed.

Funder

The National Key Technologies Research and Development Program

Key Special Technologies Research and Development Program in HenanProvince

Major Science and Technology Project in Henan Province

Key Scientific Research Project of Colleges and Universities in Henan Province

Henan Provincial Science and Technology Research Project

Publisher

PeerJ

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