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The advancement of Internet of Vehicles has brought out various vehicular applications, and some of the applications are computation-intensive or delay-sensitive. Vehicular fog computing (VFC) can provide low-latency computing service, where vehicles can share their idle computing resource among each other. However, due to the high dynamic vehicular environment, both the vehicle-to-vehicle (V2V) communication link and onboard idle computing resource are time-variant. How to ensure the efficiency of V2V computation offloading under such environment is one main challenge. In this work, we design a multi-task offloading model that considers the dynamic of V2V communication link and the computing resource allocation in vehicles. In order to make the policy of task offloading adapt to the dynamic environment, we propose a deep reinforcement learning (DRL)-based V2V computation offloading algorithm …