1. Learning Multi-agent Multi-machine Tending by Mobile Robots
- Author
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Abdalwhab, Abdalwhab, Beltrame, Giovanni, Kahou, Samira Ebrahimi, and St-Onge, David
- Subjects
Computer Science - Robotics ,Computer Science - Machine Learning - Abstract
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., Comment: 7 pages, 4 figures
- Published
- 2024