用多目标强化学习优化搜索效率，提升算法性能。
An exploration for higher efficiency in multi objective optimisation with reinforcement learning
- 基于多目标强化学习构建通用优化框架，动态选择操作算子序列。
- 通过分阶段设计验证了该方法在多目标优化中的高效性。
- 适合研究多目标优化与强化学习交叉应用的学者参考。
优化与搜索过程的效率仍是影响优化算法性能与应用的关键挑战。使用一组操作算子而非单一算子处理邻域内移动操作具有潜力，但最优或近似最优的操作算子序列仍需深入研究。一种有前景的方法是泛化经验并探索其利用方式。尽管单目标优化中已有大量相关工作，多目标场景在此方面研究较少。基于多目标强化学习的通用方法似乎能有效解决这一问题，并提供良好解决方案。本文综述了一种已完成若干阶段、尚有部分阶段待完成的泛化方法，旨在展示多目标强化学习在提升优化效率方面的潜力。
原文摘要 · Abstract (English)
Efficiency in optimisation and search processes persists to be one of the challenges, which affects the performance and use of optimisation algorithms. Utilising a pool of operators instead of a single operator to handle move operations within a neighbourhood remains promising, but an optimum or near optimum sequence of operators necessitates further investigation. One of the promising ideas is to generalise experiences and seek how to utilise it. Although numerous works are done around this issue for single objective optimisation, multi-objective cases have not much been touched in this regard. A generalised approach based on multi-objective reinforcement learning approach seems to create remedy for this issue and offer good solutions. This paper overviews a generalisation approach proposed with certain stages completed and phases outstanding that is aimed to help demonstrate the efficiency of using multi-objective reinforcement learning.
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