%0 Journal Article
%T A review of reinforcement learning based hyper-heuristics.
%A Li C
%A Wei X
%A Wang J
%A Wang S
%A Zhang S
%J PeerJ Comput Sci
%V 10
%N 0
%D 2024
%M 38983203
%F 2.411
%R 10.7717/peerj-cs.2141
%X 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.