  Vehicular edge computing (VEC) is an emerging technology with significant
potential in the field of internet of vehicles (IoV), enabling vehicles to
perform intensive computational tasks locally or offload them to nearby edge
devices. However, the quality of communication links may be severely
deteriorated due to obstacles such as buildings, impeding the offloading
process. To address this challenge, we introduce the use of Reconfigurable
Intelligent Surfaces (RIS), which provide alternative communication pathways to
assist vehicular communication. By dynamically adjusting the phase-shift of the
RIS, the performance of VEC systems can be substantially improved. In this
work, we consider a RIS-assisted VEC system, and design an optimal scheme for
local execution power, offloading power, and RIS phase-shift, where random task
arrivals and channel variations are taken into account. To address the scheme,
we propose an innovative deep reinforcement learning (DRL) framework that
combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing
RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy
Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user
(VU). Simulation results show that our proposed scheme outperforms the
traditional centralized DDPG, Twin Delayed Deep Deterministic Policy Gradient
(TD3) and some typical stochastic schemes.
