Learning Multi-agent Multi-machine Tending by Mobile Robots
<|reference_start|>Learning Multi-agent Multi-machine Tending by Mobile Robots: Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.<|reference_end|>
@article{abdalwhab2024learning,
    title={Learning Multi-agent Multi-machine Tending by Mobile Robots},
    author={Abdalwhab Abdalwhab, Giovanni Beltrame, Samira Ebrahimi Kahou, and
  David St-Onge},
    journal={arXiv preprint arXiv:2408.16875},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16875},
    primaryClass={cs.RO cs.LG}
}