用深度强化学习优化VR/AR多路径传输，提升网络适应性。
Adaptive Context-Aware Multi-Path Transmission Control for VR/AR Content: A Deep Reinforcement Learning Approach
- 基于深度强化学习动态管理多路径，实时调整传输策略。
- 在多种网络环境下实现更优带宽分配与路径切换。
- 适合高要求的VR/AR流媒体应用，提升用户体验。
本文提出自适应上下文感知多路径传输控制协议（ACMPTCP），旨在优化多路径传输控制协议（MPTCP）在数据密集型应用（如增强现实和虚拟现实流媒体）中的性能。ACMPTCP通过深度强化学习实现敏捷的端到端路径管理与最优带宽分配，在不同网络环境中支持路径重构，克服了传统MPTCP的局限性。
原文摘要 · Abstract (English)
This paper introduces the Adaptive Context-Aware Multi-Path Transmission Control Protocol (ACMPTCP), an efficient approach designed to optimize the performance of Multi-Path Transmission Control Protocol (MPTCP) for data-intensive applications such as augmented and virtual reality (AR/VR) streaming. ACMPTCP addresses the limitations of conventional MPTCP by leveraging deep reinforcement learning (DRL) for agile end-to-end path management and optimal bandwidth allocation, facilitating path realignment across diverse network environments.
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