Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation. In a platoon, a vehicle could play as a requester that employs another vehicles as performers for workload processing. An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making, which avoids the information collection from the performers and preserves their privacy. We model the interactions among the requester (leader) and multiple performers (followers) as a Stackelberg game. The requester incentivizes the performers to accept the workloads. We derive the Stackelberg equilibrium under complete information. Furthermore, deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers’ information private. Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.