Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation
Steve Paul,Nathan Maurer,Souma Chowdhury,Steve Paul,Nathan Maurer,Souma Chowdhury
Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipartite graph matching methods. These methods often assume a form that lends better explainability compared to an end-to-end (learnt) neural network based policy for MRTA. However, der...
