Deep Distributional Reinforcement Learning-Based Adaptive Routing With Guaranteed Delay Bounds

Abstract

Real-time applications that require timely data delivery over wireless multi-hop networks within specified deadlines are growing increasingly. Effective routing protocols that can guarantee real-time QoS are crucial, yet challenging, due to the unpredictable variations in end-to-end delay caused by unreliable wireless channels. In such conditions, the upper bound on the end-to-end delay, i.e., worst-case end-to-end delay, should be guaranteed within the deadline. However, existing routing protocols with guaranteed delay bounds cannot strictly guarantee real-time QoS because they assume that the worst-case end-to-end delay is known and ignore the impact of routing policies on the worst-case end-to-end delay determination. In this paper, we relax this assumption and propose DDRL-ARGB, an Adaptive Routing with Guaranteed delay Bounds using Deep Distributional Reinforcement Learning (DDRL). DDRL-ARGB adopts DDRL to jointly determine the worst-case end-to-end delay and learn routing policies. To accurately determine worst-case end-to-end delay, DDRL-ARGB employs a quantile regression deep Q-network to learn the end-to-end delay cumulative distribution. To guarantee real-time QoS, DDRL-ARGB optimizes routing decisions under the constraint of worst-case end-to-end delay within the deadline. To improve traffic congestion, DDRL-ARGB considers the network congestion status when making routing decisions. Extensive results show that DDRL-ARGB can accurately calculate worst-case end-to-end delay, and can strictly guarantee real-time QoS under a small tolerant violation probability against two state-of-the-art routing protocols.