Learning to Solve the Multi-Agent Task Assignment Problem for Automated Data Centers

Christelle Loiodice, Sofia Michel, Darko Drakulic, Jean-Marc Andreoli

Abstract: 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.

External IDs:dblp:conf/iros/LoiodiceMDA25