Deep Reinforcement Learning for Intelligent Routing in Computer Networks: A Comprehensive Review
Abstract
Routing is one of the fundamental functions of computer networks, directly influencing latency, throughput, reliability, congestion, and overall network efficiency. Conventional routing protocols generally rely on predefined metrics and algorithms that may struggle to adapt to highly dynamic network conditions. Deep Reinforcement Learning (DRL) offers a promising alternative by enabling intelligent agents to learn routing policies through interaction with network environments. This review provides a comprehensive examination of DRL-based routing techniques proposed for wired networks, wireless networks, software-defined networks, mobile networks, and emerging 5G/6G infrastructures. Different reinforcement learning algorithms, including value-based, policy-based, actor-critic, and multi-agent approaches, are analyzed. The review compares their state representations, action spaces, reward functions, training strategies, and performance metrics. Challenges associated with convergence, scalability, training overhead, exploration, partial observability, and real-world deployment are discussed. The paper also examines the emerging integration of DRL with graph neural networks and digital twins. Finally, future research directions toward adaptive, distributed, and autonomous network routing are presented
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