An EdgeAssisted Vehicle Routing Method Based on GameTheoretic Multiagent Learning Traffic congestion is a serious social issue confronting modern cities. To improve traffic efficiency, route planning for the individual vehicle based on dynamic traffic conditions has been well investigated. However, existing studies usually model the vehicles with perfect rationality and omit the complex routing interaction among multiple vehicles, causing that the routing performance dramatically deviates from the system optimum. Considering the mutual influence in multivehicle routing, we propose an edgeassisted distributed routing framework, which enables virtual agents on behalf of vehicles to interact with others to make realtime routing decisions. In the framework, we model the agent interaction as a population game and propose a scalable multiagent learning algorithm to efficiently find the Nash equilibria. Specifically, the multiagent learning algorithm takes the bounded rationality of individuals into account and uses a stage learning algorithm based on bestresponse dynamics to plan a route for vehicles. The simulation results show that the algorithm efficiently converges to Nash equilibria, and achieves the maximum throughput at the intersection and the minimum average travel time for vehicles.

MultiAgent Deep Reinforcement Learning for Cooperative Driving in Crowded Traffic Scenarios For autonomous vehicles, lane changes on crowded roads are difficult to be performed without interactions and cooperation between vehicles. This paper proposes a novel method to learn interaction and cooperate between the multiple vehicles to solve the complex traffic problem through MultiAgent Reinforcement Learning (MARL). The proposed network is designed based on the interaction network to learn optimal control strategies considering interaction between vehicles. By applying the proposed algorithm, the network can control and train the agents regardless of the number of agents. It is a practical advantage because the number of the vehicles is constantly changed in the real environment. The proposed method is evaluated in the connected car environment where all vehicles can exchange information with each other.