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RL-Based Resource Allocation in SDN-Enabled 6G Networks 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/11666 https://www.mdpi.com/1999-5903/17/11/497 И. Радосављевић P. Bojović Ž. Bojović Dynamic and efficient resource allocation is critical for Software-Defined Networking (SDN) enabled sixth-generation (6G) networks to ensure adaptability and optimized utilization of network resources. This paper proposes a reinforcement learning (RL)-based framework that integrates an actor–critic model with a modular SDN interface for fine-grained, queue level bandwidth scheduling. The framework further incorporates a stochastic traffic generator for training and a virtualized multi-slice platform testbed for a realistic beyond-5G/6G evaluation. Experimental results show that the proposed RL model significantly outperforms a baseline forecasting model: it converges faster, showing notable improvements after 240 training epochs, achieves higher cumulative rewards, and reduces packet drops under dynamic traffic conditions. Moreover, the RL-based scheduling mechanism exhibits improved adaptability to traffic fluctuations, although both approaches face challenges under node outage conditions. These findings confirm that queue-level reinforcement learning enhances responsiveness and reliability in 6G networks, while also highlighting open challenges in fault-tolerant scheduling. article https://doi.org/10.3390/fi17110497 17 11 1 19 1999-5903 Future Internet