Abstract: To address the task offloading decision problem in a dynamic Internet of Vehicles (IoV) network environment, a three-tier task offloading network architecture consisting of vehicles, multiple edge servers, and cloud servers is  proposed. Secondly, the task offloading decisions problem with the aim of minimizing latency, energy consumption, and enhancing service quality (TOD-LEQ) in this network is defined.  Thirdly, the TOD-LEQ is modeled as a Markov decision process (MDP) model. Finally, in order to cope with the impact of dynamically changing environments on task offloading in telematics, a meta learning-based distributed reinforcement learning task offloading (ME-DRO) algorithm is proposed, which can be executed in multiple threads to seek the optimal task offloading decision. Simulation results show that the ME-DRO algorithm significantly outperforms other baseline algorithms in terms of latency, quality of service and convergence speed.

Key words: Internet of Vehicles, task offloading, Markov decision process, meta learning