Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning
<|reference_start|>Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning: In this paper, we study the shortest path problem (SPP) with multiple source-destination pairs (MSD), namely MSD-SPP, to minimize average travel time of all shortest paths. The inherent traffic capacity limits within a road network contributes to the competition among vehicles. Multi-agent reinforcement learning (MARL) model cannot offer effective and efficient path planning cooperation due to the asynchronous decision making setting in MSD-SPP, where vehicles (a.k.a agents) cannot simultaneously complete routing actions in the previous time step. To tackle the efficiency issue, we propose to divide an entire road network into multiple sub-graphs and subsequently execute a two-stage process of inter-region and intra-region route planning. To address the asynchronous issue, in the proposed asyn-MARL framework, we first design a global state, which exploits a low-dimensional vector to implicitly represent the joint observations and actions of multi-agents. Then we develop a novel trajectory collection mechanism to decrease the redundancy in training trajectories. Additionally, we design a novel actor network to facilitate the cooperation among vehicles towards the same or close destinations and a reachability graph aimed at preventing infinite loops in routing paths. On both synthetic and real road networks, our evaluation result demonstrates that our approach outperforms state-of-the-art planning approaches.<|reference_end|>
@article{yin2024cooperative,
    title={Cooperative Path Planning with Asynchronous Multiagent Reinforcement
  Learning},
    author={Jiaming Yin, Weixiong Rao, Yu Xiao and Keshuang Tang},
    journal={arXiv preprint arXiv:2409.00754},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00754},
    primaryClass={cs.AI}
}