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Comparison of Market-based and DQN methods for Multi-Robot processing Task Allocation (MRpTA)

Paul Gautier 1 Laurent Johann 1 Jean-Philippe Diguet 1
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Multi-robot task allocation (MRTA) problems require that robots take complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.
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Contributor : Jean-Philippe Diguet <>
Submitted on : Wednesday, March 18, 2020 - 1:34:23 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:28 PM
Long-term archiving on: : Friday, June 19, 2020 - 1:25:03 PM


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  • HAL Id : hal-02510990, version 1


Paul Gautier, Laurent Johann, Jean-Philippe Diguet. Comparison of Market-based and DQN methods for Multi-Robot processing Task Allocation (MRpTA). IEEE International Conference on Robotic Computing (IRC), Nov 2020, Taichung, Taiwan. ⟨hal-02510990⟩



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