一种基于DQN的无人驾驶任务卸载策略

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-10 Cooperative journals: 《计算机应用研究》

王锦 张新有

Abstract： Due to its limited battery life and computing power, driverless cars are difficult to meet the processing needs of some delay-sensitive tasks or intensive tasks while ensuring battery life. To solve this problem, in the context of Mobile Edge Computing (MEC) , this paper proposed an driverless task offloading policy based on deep Q-network (DQN) . First, this paper defined a "vehicle-edge-cloud" cooperative task offloading model based on task priority, which needs to jointly optimize the computing power of the vehicle and the task offloading policy to obtain the minimum delay and energy consumption of the system. Since the problem is a mixed-integer nonlinear programming problem and is NP-hard, this paper solved it in two steps—the first step obtained the analytical solution for the optimal computation power of vehicle through mathematical derivation, and then, under the fixed numerical value condition, the DQN algorithm obtained the optimal offloading strategy of the task. Finally, this paper established a simulation model by integrating tools such as SUMO, Pytorch and Python. This paper compared the DQN algorithm and the other three algorithms under different task loads, MEC server computation powers and energy consumption weight co-efficients. The experimental results verify the feasibility and superiority of the proposed policy.

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