基于多智能体强化学习的车联网资源分配策略  

Resource allocation strategy for internet of vehicles based on multi-agent reinforcement learning

作　　者：谢弘明 彭大芹[1] 赵梦青 XIE Hongming;PENG Daqin;ZHAO Mengqing(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《微电子学与计算机》2024年第11期22-30,共9页Microelectronics & Computer

基　　金：国家自然科学基金(62171072)。

摘　　要：车辆高动态变化场景下的传统车联网无线资源分配方法难以满足高质量通信需求,尤其在交叉路口等拥堵地点存在资源调度效率低下、网络拥堵、时延高等问题。针对上述问题,提出了基于注意力机制的多智能体强化学习策略,旨在通过改进多智能体训练模型设计,增强模型对多智能体的合理调度和资源分类能力。通过引入基于注意力机制学习多个智能体之间的依赖关系,构建多视角的全局状态语义特征,增强多智能体强化学习策略训练过程中全局值函数的策略评估能力。仿真验证表明,提出的方法在频谱共享任务中有效提升了策略的资源分配能力,提高信道总容量和车间通信链路的有效负载的传递成功率。The traditional wireless resource allocation methods for internet of vehicles in highly dynamic change scenarios cannot meet the high-quality communication demand,especially in congested locations such as intersections,where there are problems such as inefficient resource scheduling,network congestion,and high time budget.To address the above problems,a multi-intelligence reinforcement learning strategy based on the attention mechanism is proposed,aiming to enhance the model's ability to reasonably schedule and classify resources for multiple intelligence by improving the design of the multi-intelligence training model.The paper introduces an attention-based mechanism to learn the dependency relationship between multiple intelligence,constructs global state semantic features with multiple perspectives,and enhances the strategy evaluation ability of global value function during the training process of multi-intelligence reinforcement learning strategy.Simulation validation shows that the proposed method effectively enhances the resource allocation capability of the policy in the spectrum sharing task and improves the delivery success rate of the total channel capacity and the payload of the inter-vehicle communication link.

关 键 词：车联网 多智能体强化学习 资源分配 

分 类 号：TN929.5[电子电信—通信与信息系统]