Multiagent DRL Using Prioritized Experience Replay and Dynamic Variance Noise for Task Offloading and Power Manipulation in VEC

Abstract

Vehicular edge computing (VEC) is viewed as an important component for vehicle networks due to the prevalence of smart vehicles. However, stringent requirements such as ultralow latency and minimal energy consumption should be achieved for the emerging transmission-and-computation-intensive applications due to the limited communication and computing resources. To address these challenges, this article proposes a multiagent priority-based twin-delayed deep deterministic policy gradient (MAPTD3) approach for the joint optimization of task offloading and power allocation in a VEC system at the millimeter-wave (mmWave) band. First, the joint task offloading and power allocation problem is formulated as a nonconvex mixed-integer nonlinear program (MINLP) with a multiplicative objective function subject to delay and power constraints. Second, the proposed approach integrates two key techniques: a dynamic variance noise (DVN) mechanism for adaptive exploration and prioritized experience replay (PER) to enhance sample efficiency. Finally, it is shown in the simulation results that MAPTD3 consistently outperforms existing benchmarks with 96%–99% optimality ratios (ORs) under various load scenarios.