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

Paul Gautier, J. Laurent, J. Diguet
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Abstract

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.
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DQN作为多机器人处理任务分配(MRpTA)市场方法的替代方案
多机器人任务分配(MRTA)问题要求机器人基于对动态和不确定环境的理解做出复杂的选择。作为一个分布式计算系统,多机器人系统必须处理和分配处理任务。每个机器人都必须在对环境有限了解的基础上为系统的整体效率做出贡献。基于市场的方法是在MRS上处理处理任务的自然候选方法,但最近强化学习,特别是深度q -网络(DQN)的大量发展为解决问题提供了新的机会。在本文中,我们提出了一种新的基于dqn的方法,使机器人可以直接从经验中学习,并将其与基于市场的方法以及集中式和纯局部解决方案进行比较。我们的研究显示了基于学习的方法的相关性,也突出了解决MRS中处理负载平衡问题的研究挑战。
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