{Reference Type}: Journal Article
{Title}: A review of reinforcement learning based hyper-heuristics.
{Author}: Li C;Wei X;Wang J;Wang S;Zhang S;
{Journal}: PeerJ Comput Sci
{Volume}: 10
{Issue}: 0
{Year}: 2024
{Factor}: 2.411
{DOI}: 10.7717/peerj-cs.2141
{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.