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19. 10. 2025.
Learning to Solve the Multi-Agent Task Assignment Problem for Automated Data Centers
We consider a large-scale data center where a fleet of heterogeneous mobile robots and human workers collaborate to handle various installation and maintenance tasks. We focus on the underlying multi-agent task assignment problem which is crucial to optimize the overall system. We formalize the problem as a Markov Decision Process and propose an end-to-end learning approach to solve it. We demonstrate the effectiveness of our approach in simulation with realistic data and in the presence of uncertainty.