Graph Neural Networks with Model-based Reinforcement Learning for Multi-agent Systems
<|reference_start|>Graph Neural Networks with Model-based Reinforcement Learning for Multi-agent Systems: Multi-agent systems (MAS) constitute a significant role in exploring machine intelligence and advanced applications. In order to deeply investigate complicated interactions within MAS scenarios, we originally propose "GNN for MBRL" model, which utilizes a state-spaced Graph Neural Networks with Model-based Reinforcement Learning to address specific MAS missions (e.g., Billiard-Avoidance, Autonomous Driving Cars). In detail, we firstly used GNN model to predict future states and trajectories of multiple agents, then applied the Cross-Entropy Method (CEM) optimized Model Predictive Control to assist the ego-agent planning actions and successfully accomplish certain MAS tasks.<|reference_end|>
@article{chen2024graph,
    title={Graph Neural Networks with Model-based Reinforcement Learning for
  Multi-agent Systems},
    author={Hanxiao Chen},
    journal={arXiv preprint arXiv:2407.09249},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09249},
    primaryClass={cs.MA cs.AI}
}