A Hybrid Soft Asynchronous Actor-critic Framework for UAV-assisted IoV Task Offloading with Vehicle Trajectory Prediction

With the rapid increase in computing demand for the Internet of Vehicles (IoV), the limited on-board computing resources have gradually become a bottleneck restricting the efficient processing of tasks. Unmanned aerial vehicles (UAVs), with their high mobility and flexible deployment capabilities, have become an ideal platform for assisting IoV task offloading. However, the dynamic environment and the mobility of vehicles pose significant challenges for task offloading and resource allocation in UAV-assist IoV networks. This paper proposes a soft asynchronous actor-critic (SAAC) framework with vehicle trajectory prediction based on deep reinforcement learning (DRL) for UAV-assisted IoV task offloading. Firstly, in order to solve the problem of poor stability of traditional multi-agent deep reinforcement learning algorithms in dynamic environments, this paper combines the asynchronous training of multi-agent systems with the soft update and target network mechanism to improve the anti-interference and stability of the algorithm. Secondly, a module based on a recurrent neural network (RNN) integrated with a sliding window mechanism is proposed to address the uncertainty arising from vehicle mobility. This module enables precise prediction of vehicle trajectories, thereby facilitating forward-looking flight strategy planning for UAVs and reducing task transmission delays. Simulation results show that the proposed algorithm is effective, therefore this paper provides a new framework for the UAV-assisted IoV task offloading.