Silent Neuron 理论与 ReSiN 方法：保持深度强化学习可塑性以优化自适应视频流
Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
AI 导读
研究者发现自适应视频流中的神经网络存在可塑性丧失问题，现有 dormant neuron 指标不足以刻画这一退化，据此提出 Silent Neuron 理论和通过前向与反向传播状态引导神经元重置的 ReSiN 方法，并给出非平稳网络条件下更紧的性能界。
正文 · 原文
Abstract:Adaptive video streaming optimizes Quality of Experience (QoE) metrics by selecting appropriate bitrates according to varying network bandwidth and user demands. In practice, however, real-world network bandwidth often exhibits heterogeneity relative to training environments. Current methods predominantly tackle this problem through learning-based approaches designed to improve generalization performance. Our systematic investigation reveals a critical limitation: neural networks suffer from plasticity loss, significantly impeding their ability to adapt to heterogeneous network conditions. Through theoretical analysis of neural propagation mechanisms, we demonstrate that existing dormant neuron metrics inadequately characterize neural plasticity loss. To address this limitation, we have developed the Silent Neuron theory, which provides a more comprehensive framework for understanding plasticity degradation. Based on these theoretical insights, we propose the Reset Silent Neuron (ReSiN), which preserves neural plasticity through strategic neuron resets guided by both forward and backward propagation states. Moreover, we establish a tighter performance bound for ReSiN under non-stationary network conditions. In our implementation of an adaptive video streaming system, ReSiN shows substantial gains under non-stationary conditions in which the available bandwidth alternates between high and low regimes, improving the QoE by 178%, and it remains beneficial under stationary real-world traces, where it improves the QoE by 1.6%. Code will be available: this https URL
| Comments: | This paper has been accepted by IEEE Transactions on Mobile Computing |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2505.01584 [cs.LG] |
| (or arXiv:2505.01584v5 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2505.01584 arXiv-issued DOI via DataCite |
Submission history
From: Zhiqiang He [view email]
[v1]
Fri, 2 May 2025 21:03:03 UTC (10,839 KB)
[v2]
Tue, 6 May 2025 02:36:10 UTC (10,839 KB)
[v3]
Thu, 14 May 2026 04:29:35 UTC (19,404 KB)
[v4]
Wed, 8 Jul 2026 10:04:35 UTC (18,806 KB)
[v5]
Wed, 11 Nov 2026 07:00:26 UTC (18,846 KB)
来源：arXiv cs.AI · arxiv.org