Reinforcement Learning-Based Cooperative Control for Connected and Autonomous Vehicles

With the rapid advancement of intelligent vehicle technologies, decision-making and control methods based on single-agent systems face significant challenges in managing increasingly complex traffic environments. This presentation exploits cutting-edge methodologies for heterogeneous multi-agent collaborative learning, integrating reinforcement learning, distributed optimization, and system control theories. In multimodal and multi-source data environments, robust strategies are elaborated for achieving efficient communication, real-time data sharing, and collaborative control among various vehicles and infrastructures. By referencing widely recognized research findings and incorporating our latest work in optimal vehicle control, this presentation further elucidates the application of mature heterogeneous multi-agent reinforcement learning methods to enhance cooperative driving processes. Additionally, this presentation provides an extended discussion on architectural design, algorithm selection, and practical deployment, emphasizing reliability under uncertain operational conditions. Ultimately, this presentation aims to establish advanced heterogeneous multi-agent collaborative control strategies, fostering the development of an efficient, robust, and scalable ecosystem for heterogeneous intelligent vehicle collaboration.