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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.
摘要：
基于强化学习的超启发式算法（RL-HH）是优化领域的流行趋势。RL-HH结合了超启发式（HH）的全局搜索能力和强化学习（RL）的学习能力。这种协同作用允许代理动态调整自己的策略，导致解决方案的逐步优化。现有研究表明RL-HH在解决复杂现实问题方面的有效性。然而,对RL-HH领域的全面介绍和总结尚属空白。本研究回顾了目前存在的RL-HH，并提出了RL-HH的一般框架。本文将算法类型分为两类：基于价值的强化学习超启发式和基于策略的强化学习超启发式。对每个类别中的典型算法进行了总结和详细描述。最后,讨论了RL-HH现有研究的不足和未来的研究方向。
