The field of network optimization and information dissemination is rapidly evolving, with a focus on developing innovative solutions to improve the efficiency and effectiveness of data transmission. Recent research has explored the use of reinforcement learning and multi-agent systems to optimize network performance, particularly in vehicular networks and edge computing scenarios. These approaches have shown significant promise in reducing latency, improving resource utilization, and enhancing overall quality of service. Notable papers in this area include:

• Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI, which introduces an Age-and-Value-Aware metric to capture the timeliness and task relevance of service information.
• Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks, which proposes a novel framework for optimizing agent migration in vehicular networks.
• VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network, which introduces a joint optimization framework for control and communication in vehicular digital twin networks.