Abdalwhab, Abdalwhab Bakheet Mohamed, Beltrame, Giovanni, Ebrahimi Kahou, Samira et St-Onge, David.
2025.
« Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots ».
AI, vol. 6, nº 10.
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Résumé
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 greatly 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. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Ebrahimi-Kahou, Samira St-Onge, David |
| Affiliation: | Génie logiciel et des technologies de l'information, Génie mécanique |
| Date de dépôt: | 07 oct. 2025 12:10 |
| Dernière modification: | 07 oct. 2025 19:19 |
| URI: | https://espace2.etsmtl.ca/id/eprint/32329 |
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