Adaptive Bandwidth Allocation via Uncertainty-Constrained Deep Reinforcement Learning.

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Bibliographic Details
Title:	Adaptive Bandwidth Allocation via Uncertainty-Constrained Deep Reinforcement Learning.
Authors:	Wei, Li¹ liweisz@js.sgcc.com.cn, Yong, Wu¹ wuy11@js.sgcc.com.cn, Dong, Yan¹ yandongsz@js.sgcc.com.cn
Source:	Computer Science & Information Systems. Jan2026, Vol. 23 Issue 1, p277-297. 21p.
Subjects:	Bandwidth allocation, Reinforcement learning, Telecommunications services, Resource allocation, Machine learning
Abstract:	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 uncertainty conditions. To address this, we propose a novel algorithm named UncertaintyConstrained 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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	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 uncertainty conditions. To address this, we propose a novel algorithm named UncertaintyConstrained 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. [ABSTRACT FROM AUTHOR]
ISSN:	18200214
DOI:	10.2298/CSIS250923008L