  This paper considers minimizing the age-of-information (AoI) and transmit
power consumption in a vehicular network, where a roadside unit (RSU) provides
timely updates about a set of physical processes to vehicles. We consider
non-orthogonal multi-modal information dissemination, which is based on
superposed message transmission from RSU and successive interference
cancellation (SIC) at vehicles. The formulated problem is a multi-objective
mixed-integer nonlinear programming problem; thus, a Pareto-optimal front is
very challenging to obtain. First, we leverage the weighted-sum approach to
decompose the multi-objective problem into a set of multiple single-objective
sub-problems corresponding to each predefined objective preference weight.
Then, we develop a hybrid deep Q-network (DQN)-deep deterministic policy
gradient (DDPG) model to solve each optimization sub-problem respective to
predefined objective-preference weight. The DQN optimizes the decoding order,
while the DDPG solves the continuous power allocation. The model needs to be
retrained for each sub-problem. We then present a two-stage
meta-multi-objective reinforcement learning solution to estimate the Pareto
front with a few fine-tuning update steps without retraining the model for each
sub-problem. Simulation results illustrate the efficacy of the proposed
solutions compared to the existing benchmarks and that the meta-multi-objective
reinforcement learning model estimates a high-quality Pareto frontier with
reduced training time.
