  Edge computing plays an essential role in the vehicle-to-infrastructure (V2I)
networks, where vehicles offload their intensive computation tasks to the
road-side units for saving energy and reduce the latency. This paper designs
the optimal task offloading policy to address the concerns involving processing
delay, energy consumption and edge computing cost. Each computation task
consisting of some interdependent sub-tasks is characterized as a directed
acyclic graph (DAG). In such dynamic networks, a novel hierarchical Offloading
scheme is proposed by leveraging deep reinforcement learning (DRL). The
inter-dependencies among the DAGs of the computation tasks are extracted using
a graph neural network with attention mechanism. A parameterized DRL algorithm
is developed to deal with the hierarchical action space containing both
discrete and continuous actions. Simulation results with a real-world car speed
dataset demonstrate that the proposed scheme can effectively reduce the system
overhead.
