An End-to-End Task Allocation Framework for Autonomous Mobile Systems

—This work aims to unravel the problem of task allocation and planning for multi-agent systems with a particular interest in promoting adaptability. We proposed a novel end-to-end task allocation framework employing reinforcement learning methods to replace the handcrafted heuristics used in previous works. The proposed framework achieves high adaptability and also explores more competitive results. Learning experiences from the feedback help to reach the advantages. The systematic objectives are adjustable and responsive to the reward design intuitively. The framework is validated in a set of tests with various parameter settings, where adaptability and performance are demonstrated.