# Transfer Reinforcement Learning for Combinatorial Optimization Problems
## 6 Conclusions
This publication has emanated from research supported in part by a grant from Science Foundation Ireland under Grant number 18/CRT/6049 (Author: [PERSON]). For the purpose of Open Access, the author ([PERSON]) has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. [PERSON] was supported by Brazilian Research Agencies: CNPq/INERGE (Grant No. 465704/2014-0), CNPq (Grant No. 425509/2018-4) and FAPEMIG (Grant No. APQ-00870-17). This publication has granted from research conducted with the financial support of Science Foundation Ireland under Grant number 21/FFP-P/10065. The dataset analyzed during the current study is available from the TSPLIB repository ([[http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)]([http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)), accessed on 1 December 2023). The authors are grateful to UFRB, Maynooth University, UFSJ, FAPEMIG, CNPq/INERGE and Science Foundation Ireland. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} \(RL\) & Reinforcement learning \\ \(ML\) & Machine learning \\ \(AutoML\) & Automated machine learning \\ \(AutoRL\) & Automated reinforcement learning \\ \(TL\) & Transfer learning \\ \(TSP\) & Traveling salesman problem \\ \(ATSP\) & Asymmetric traveling salesman problem \\ \(SOP\) & Sequential ordering problem \\ \(TSPLIB\) & Traveling Salesman Problem Library \\ \(Auto\_TL\_RL\) & automated transfer reinforcement learning algorithm \\ \(MDP\) & Markov decision processes \\ \(S\) & State \\ \(s\) & Current state \\ \(s^{\prime}\) & New state \\ \(A\) & Action \\ \(a\) & Current action \\ \(a^{\prime}\) & New action \\ \(R\) & Reinforcements \\ \(T\) & State transition model \\ \(t\) & Statistical test \\ \(Q\) & Learning matrix \\ \(Q_{t}\) & Learning matrix at the current time \\ \end{tabular}   
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