文献类型：期刊文献

英文题名：VN-MADDPG: A Variable-Noise-Based Multi-Agent Reinforcement Learning Algorithm for Autonomous Vehicles at Unsignalized Intersections

作者：Zhang, Hao[1];Du, Yu[1];Zhao, Shixin[1];Yuan, Ying[1];Gao, Qiuqi[1]

通讯作者：Du, Y[1]

机构：[1]Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;[1141739]北京联合大学机器人学院;

年份：2024

卷号：13

期号：16

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85202684299);WOS:【SCI-EXPANDED(收录号:WOS:001305610300001)】；

基金：This research was funded by the Vehicle-Road Cooperative Autonomous Driving Fusion Control Project.

语种：英文

外文关键词：multi-agent model; autonomous driving decision making; intersection scenarios; variable noise

摘要：The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The variable-noise mechanism reduces noise dynamically, enabling the agent to utilize the learned policy more effectively to complete tasks. This significantly improves the stability of the decision-making model in making optimal decisions. The importance sampling module addresses the inconsistency between outdated experience in the replay buffer and current environmental features. This enhances the model's learning efficiency and improves the robustness of the decision-making model. Experimental results on the CARLA simulation platform show that the success rate of decision making at unsignalized intersections by autonomous vehicles has significantly increased, and the pass time has been reduced. The decision-making model based on the VN-MADDPG algorithm demonstrates stable and excellent decision-making performance.