An Efficient Attribute Attention-based Vehicle Routing Algorithm with Adaptive Training Strategy

As a highly flexible communication paradigm, Vehicular Ad-hoc Networks (VANETs) are widely employed in both civil and military applications. Existing deep reinforcement learning-based data forwarding algorithms for VANETs suffer from long training time, limited adaptive capability and so on. In order to cope with the above challenges, we propose an Efficient Attribute Attention-based Vehicle Routing Algorithm with Adaptive Training Strategy(A3VRAT). Specifically, we first propose a deep reinforcement learning framework based on designed Vehicle Attribute Attention (VAA) module, to enable rapidly adaptive data forwarding in various vehicular networking scenarios. Secondly, we design an effective deep learning training mechanism utilizing the constructed Multi-priority Dynamic Experience Pool (MDEP), to achieve rapid convergence in wide-area vehicular environments. Finally, we verify the feasibility and effectiveness of the proposed algorithm through a series of experiments.