Open AccessDQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)
Author(s) -
Paul Gautier,
Johann Laurent
Publication year - 2021
Publication title -
international journal of robotic computing
Language(s) - English
Resource type - Journals
ISSN - 2641-9521
DOI - 10.35708/rc1870-126266
Subject(s) - computer science , task (project management) , reinforcement learning , robot , artificial intelligence , distributed computing , engineering , systems engineering
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 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.