基于多智能体深度强化学习的车联网 可信任务卸载策略.

Aiming at the problem that the credibility of edge nodes in the Internet of Vehicles could not be guaranteed, this paper proposed a reputation-based task offloading and resource allocation model for the Internet of Vehicles, and used the reputation of edge nodes recorded on the blockchain to evaluate its credibility, so as to help the terminal devices select reliable edge nodes for task offloading. At the same time, this paper modeled the offloading strategy as the time delay and energy consumption minimization problem under the reputation constraint, and used the multi-agent deep deterministic policy gradient algorithm to solve the approximate optimal solution of the NP-hard problem. The edge server received rewards based on the completion of task offloading, and then updated the reputation recorded on the blockchain. Simulation experiments show that the proposed algorithm reduces in terms of time delay and energy consumption by 25.58% to 27.44% compared with the benchmark testing schemes. [ABSTRACT FROM AUTHOR]