Joint Task Scheduling and Resource Allocation for Semantic-Aware VEC: A Lyapunov-Guided Multi-Objective Reinforcement Learning Approach

Semantic-aware Vehicular Edge Computing (VEC) has emerged as a novel paradigm to significantly reduce transmission costs and edge resource consumption by offloading extracted task-driven semantic information. However, excessive semantic extraction may impose additional computational workload. In the face of unknown environmental dynamics, the semantic extraction ratio must be jointly designed with task offloading for resource-constrained VEC. To this end, we conceive a multiple-objective (MO) semantic-aware task offloading framework for VEC by jointly optimizing semantic extraction ratio, transmit power and task scheduling strategies aimed at minimizing both long-term age-of-information (AoI) and energy consumption while guaranteeing queue stability. Subsequently, we propose a Lyapunov-guided multi-objective reinforcement learning (MORL)-based semantic-aware joint task scheduling and resource allocation (SJTSRA) solution. Specifically, Lyapunov optimization method is first leveraged to transform the original problem into a multi-objective Markov decision process (MOMDP). Then, the concave-augmented Pareto Q-learning (CAPQL) algorithm is employed to find Pareto optimal solutions through introducing uniform weight sampling and entropy regularization, where the convergence can be guaranteed theoretically. Simulation results show that the proposed solution achieves the closest approximation to the Pareto front with the highest hypervolume, and superior energy-AoI trade-offs across varying environment parameters compared to all benchmarks.