TADocs: Teacher–Assistant Distillation for Improved Policy Transfer in 6G RAN Slicing

Network slicing is an advanced technology that significantly enhances network flexibility and efficiency. Recently, reinforcement learning (RL) has been applied to solve resource management challenges in 6G networks. However, RL-based network slicing solutions have not been widely adopted. One of the primary reasons for this is the slow convergence of agents when the Service Level Agreement (SLA) weight parameters in Radio Access Network (RAN) slices change. Therefore, a solution is needed that can achieve rapid convergence while maintaining high accuracy. To address this, we propose a Teacher and Assistant Distillation method based on cosine similarity (TADocs). This method utilizes cosine similarity to precisely match the most suitable teacher and assistant models, enabling rapid policy transfer through policy distillation to adapt to the changing SLA weight parameters. The cosine similarity matching mechanism ensures that the student model learns from the appropriate teacher and assistant models, thereby maintaining high performance. Thanks to this efficient matching mechanism, the number of models that need to be maintained is greatly reduced, resulting in lower computational resource consumption. TADocs improves convergence speed by 81% while achieving an average accuracy of 98%.