Adaptive Bandwidth Allocation via Uncertainty-Constrained Deep Reinforcement Learning

Li Wei ; Wu Yong ; Yan Dong

Computer Science and Information Systems, Tome 23 (2026) no. 1

Résumé

With the rapid growth of network services, traditional static bandwidth allocation schemes can no longer meet the demands of multi-user, dynamic, and QoS-sensitive applications. Ensuring both efficiency and stability in bandwidth allocation remains a significant challenge, especially under high variability and uncer-tainty conditions. To address this, we propose a novel algorithm named Uncertainty-Constrained Stability-aware Deep Reinforcement Learning (UCS-DRL) for dynamic bandwidth allocation. UCS-DRL adopts a dual-policy architecture: a task policy that learns optimal bandwidth allocation decisions, and a stability policy guided by uncertainty-aware value estimation to identify and mitigate potential risky or unstable behaviors during deployment. Furthermore, the framework incorporates a curiosity-driven exploration mechanism based on Random Network Distillation, which enhances exploration efficiency by encouraging the agent to visit informative and under-explored states. Experimental results show that UCS-DRL achieves high bandwidth utilization and service quality while reducing policy volatility and risky actions, balancing performance and robustness in dynamic bandwidth allocation.

Détail

Keywords: Dynamic Resource Allocation, Reinforcement Learning, Uncertainty Estimation, Stability-aware Control, Dual-policy Framework