# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 6 Conclusions  
This paper has presented a Transfer Reinforcement Learning (TRL) approach for accelerating the re-training process of Reinforcement Learning (RL)-based capacity sharing solutions for multi-cell scenarios when new cells are deployed in the Radio Access Network (RAN). The proposed approach allows transferring the weights of the existing capacity sharing policy for a given number of cells to build the initial artificial neural network of the policy to be used with a higher number of cells. This allows accelerating the re-training process to determine this policy.  
The behavior of the proposed TRL approach has been assessed in a multi-cell scenario where a new cell is deployed by comparing the training behavior and the performance of the trained policies with and without TRL. Results have shown that: (i) The training duration is significantly reduced when using the TRL approach for re-training, achieving convergence in around 50-80% less time in the considered scenarios. (ii) The performance of the trained policies with the TRL approach is similar to the performance when training the policies from scratch, reaching SLA satisfaction ratios of 96% and 95%, respectively. Overall, the results presented here reflect the capability of the proposed TRL solution to accelerate the re-training process of the policies when adding new cells in the scenario, obtaining good performance with reduced training durations. Finally, some considerations on the implementation of the solution have been discussed, including the integration of the solution into the O-RAN architecture and the placement of the different functionalities at the edge nodes.  
Conceptualization, I.V., J.P.-R. and O.S.; methodology, I.V., J.P.-R. and O.S.; software, I.V.; validation, I.V., J.P.-R. and O.S.; formal analysis, I.V., J.P.-R. and O.S.; investigation, I.V., J.P.-R. and O.S.; data curation, I.V.; writing--original draft preparation, I.V.; writing--review and editing, I.V., J.P.-R. and O.S.; visualization, I.V., J.P.-R. and O.S.; supervision, J.P.-R. and O.S.; project administration, J.P.-R. and O.S.; funding acquisition, J.P.-R. and O.S. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101096034 (VERGE project). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This research has also been partly funded by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union--NextGenerationEU under projects OPTIMAIX_OaaS (Ref. ISI-063000-2021-34) and OPTIMAIX_NDT (Ref. ISI-063000-2021-35). The work of [PERSON] has been funded by the European Union-NextGenerationEU, the Spanish Ministry of Universities, and the Plan for Recovery, Transformation, and Resilience, through the call for Margarita Salas Grants of the Universitat Politecnica de Catalunya (ref. 2022 UPC-MSC-94079).  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.