Physical Simulation for Multi-agent Multi-machine Tending

The manufacturing sector like many other sectors was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize Kugler (2022). Simultaneously, Reinforcement learning (RL) offers a promising solution where robots can learn to perform tasks through interaction and feedback from the environment Singh et al. (2022). However, despite their success in numerous simulation environments, we still don't see many real-world deployments of RL robotic solutions. In fact, many researchers either oversimplify the targeted real-world scenario such as Wu et al. (2023) or do not even evaluate their model in physical robots Lu et al. (2022); Na et al. (2022). It is known that training RL policies directly in real robots can be expensive, timeconsuming, labor-intensive, and maybe even dangerous, that is why it makes sense to try to leverage training in simulation.