# Transfer Learning for Operator Selection: A Reinforcement Learning Approach
## 5 Conclusions and Future Work  
This article described how transfer learning was used in a reinforcement learning-based adaptive operator selection scheme incorporated in an ABC algorithm to tackle SUKP as a combinatorial optimisation problem. The ABC algorithm uses a pool of operators from which the adaptive operator selection scheme identifies the best-fitting operator for the current state of the problem and the search conditions. This helps search through the problem space in an efficient way. The operator selection scheme is developed and fine-tuned with the Q learning algorithm embedded and empowered with the \"Hard-C-Means\" clustering algorithm. The knowledge and experience gained through this process is transferred into the next runs to be utilised for faster approximation and better-quality solutions. The experimental results demonstrated that the transferred experience across runs helped achieve slightly better solution quality but significantly faster convergence. Both scenarios of keeping learning ON and OFF are tested, and it is observed that each has its own set of advantages and disadvantages. It is clearly observed that learning through a single run helps in solving problem instances in subsequent runs in a much shorter time. This is because the gained experience is used to select more complementary operators one after another, cutting the computational time, while the quality of the solution improves slightly or at least remains the same.  
This study has considered the first level of experience and knowledge transfer in solving combinatorial optimisation problems, which implies training the agents in one run and utilising its gained experiences in the next runs. The next two levels, transfer across problem instances and problem types, remain as the future study, which is expected to achieve a significant breakthrough in building generic problem solvers. The proposed transfer learning method can be used to solve a wide range of real-world problems, which are applied to tackling in real-world applications, including but not limited to image recognition, speech recognition, and timetabling problems.  
R.D.; Methodology, software, conceptualisation, draft writing, and visualisation. M.E.A.; Supervision, methodology, conceptualisation, writing, and editing, A.R.; Writing, editing, and formal representation and analysis. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 7: Comparative results by the proposed approach, RLABC-TC, and the state-of-art methods, GA [28], BABC [32], binDE [33], and GPSO [31], on benchmark instances in Set1, Set2, and Set3.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The first author thanks the Scientific and Technological Research Council of Turkey because of their support during his post-doctoral visit to UWE under programme TUBITAK-2219. Furthermore, the numerical calculations reported in this paper were fully performed at TUBITAK ULAKBIM, High-Performance and Grid Computing Center (TRUBA resources).  
**Conflicts of Interest:** The authors declare no conflict of interest.