# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 7 Conclusions  
In this paper, a new integrated adaptive algorithm called WFQ continual-DRL was developed to enhance dynamic bandwidth allocation for each queue in a WFQ system. The proposed algorithm enables the system to obtain a policy agent that learns the optimal weights for different arrivals of network flows. Our contribution involves integrating the adopted reinforcement learning algorithm, known as the SAC algorithm, with the EWC approach, which supports continual learning in the more challenging field of DRL. To enable this implementation to be compared to the existing extensive telecommunication network, this investigation considered two scenarios: the first utilized eight queues, while the second employed twelve. The findings demonstrate that incorporating EWC effectively mitigates the issue of catastrophic forgetting in DRL models, thereby improving learning stability and the models' adaptability to new tasks without compromising prior performance. This achieved high performance by enabling efficient learning, rapid adaptation, and scalability, even when the number of queues increases. This integration significantly improved the performance of the measurement system, as reflected in cumulative reward. The results achieved an improvement in the reward curve in an experiment involving eight queues, and expanding to twelve queues appears to result in increased and smoother fluctuation of average reward, indicating improved overall performance of the MSE, and MAE and resulting in more reliable decision making within WFQ environments, particularly through dynamic bandwidth allocation. The comparative evaluation between the enhanced WFQ continual-DRL algorithm demonstrates that integrating EWC significantly improves accuracy, as indicated by a lower RMSE, and fairness, as reflected in a higher Jain's Index of the learning model. This enhancement of continual learning techniques in DRL and scheduling is important for resource allocation, mainly in complex network environments. Finally, training and evaluation of real traffic flows of the dataset indicate the agent to achieve better performance across diverse conditions, resulting in more adaptable and effective bandwidth allocation processes across twelve queues simulation, indicating that the model training is under aggressive traffic. An interesting direction for future study would involve integrating real network traffic data to effectively assess the algorithm's performance under real-world conditions in dynamic and complex network environments.  
Conceptualization, D.A.M. and M.A.M.; methodology, D.A.M. and M.A.M.; software, M.A.M.; validation, D.A.M. and M.A.M.; formal analysis, M.A.M.; investigation, M.A.M.; resources, M.A.M.; data curation, M.A.M.; writing--original draft preparation, M.A.M.; writing--review and editing, D.A.M. and M.A.M.; visualization, M.A.M.; supervision, D.A.M.; project administration, D.A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.