After decades of unprecedented development, modern networks have evolved far beyond expectations in terms of scale and complexity. In many cases, traditional traffic engineering (TE) approaches fail to address the quality of service (QoS) requirements of modern networks. In recent years, deep reinforcement learning (DRL) has proved to be a feasible and effective solution for autonomously controlling and managing complex systems. Massive growth in the use of DRL applications in various domains is beginning to benefit the communications industry. In this paper, we firstly provide a comprehensive overview of DRL-based TE. Then, we present a detailed literature review on applications of DRL for TE including three fundamental issues: routing optimization, congestion control, and resource management. Finally, we discuss our insights into the challenges and future research perspectives of DRL-based TE.

中文翻译：

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利用深度强化学习进行流量工程：调查


经过几十年前所未有的发展，现代网络的规模和复杂性远远超出了人们的预期。在许多情况下，传统的流量工程 (TE) 方法无法满足现代网络的服务质量 (QoS) 要求。近年来，深度强化学习（DRL）已被证明是自主控制和管理复杂系统的可行且有效的解决方案。 DRL 应用程序在各个领域的使用大幅增长正开始使通信行业受益。在本文中，我们首先全面概述了基于 DRL 的 TE。然后，我们对 DRL 在 TE 中的应用进行了详细的文献综述，包括三个基本问题：路由优化、拥塞控制和资源管理。最后，我们讨论了对基于 DRL 的 TE 的挑战和未来研究前景的见解。