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Article Dans Une Revue International Journal of Robotic Computing Année : 2021

DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)

Résumé

Multi-robot task allocation (MRTA) problems require that robots make 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 e ciency 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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Dates et versions

hal-03251554 , version 1 (07-06-2021)

Identifiants

  • HAL Id : hal-03251554 , version 1

Citer

Paul Gautier, Johann Laurent, Jean-Philippe Diguet. DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA). International Journal of Robotic Computing, inPress, 3 (1). ⟨hal-03251554⟩
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