Multi-Agent Deep Reinforcement Learning for Large-Scale Platoon Coordination with Partial Information at Hubs

This paper considers the hub-based platoon co-ordination problem in a large-scale transportation network, to promote cooperation among trucks and optimize the overall efficiency of the transportation network. We design a distributed communication model for transportation networks and transform the problem into a Dec-POMDP (Decentralized-Partial Observable Markov Decision Process). We then propose an A-QMIX deep reinforcement learning algorithm to solve the problem, which adopts centralized training and distributed execution and hence provides a reliable model for trucks to make quick decisions using only partial information. Finally, we carry out experiments with 100 trucks in the transportation network of the Yangtze River Delta region in China to demonstrate the effectiveness of the proposed algorithm.