Factory Manipulation with Cooperative Multi-Agent Reinforcement Learning

Paper, GitHub

Efficient factory automation is crucial for modern manufacturing, but traditional pre-programmed approaches often fall short in handling dynamic and complex tasks. This report investigates the application of cooperative multi-agent reinforcement learning to address these challenges in a simulated factory environment. By training multiple robot arms to collaborate in transporting and sorting objects, we aim to optimise efficiency and adaptability in factory manipulation. Our results show that training successfully cooperating agents is hard and time-consuming as well and can, up to now, only be achieved for a simplified problem setting