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Open AccessArticle

Multi-Agent Reinforcement Learning Using Linear Fuzzy Model Applied to Cooperative Mobile Robots

1
Department of industrial engineering and manufacturing, Autonomous University of Ciudad Juarez, Ciudad Juarez 32310, Mexico
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Faculty of mechanical and electrical engineering, Autonomous University of Coahuila, Torreon 27276, Mexico
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Author to whom correspondence should be addressed.
Symmetry 2018, 10(10), 461; https://doi.org/10.3390/sym10100461
Received: 5 September 2018 / Revised: 24 September 2018 / Accepted: 30 September 2018 / Published: 3 October 2018
(This article belongs to the Special Issue Symmetry in Complex Systems)
A multi-agent system (MAS) is suitable for addressing tasks in a variety of domains without any programmed behaviors, which makes it ideal for the problems associated with the mobile robots. Reinforcement learning (RL) is a successful approach used in the MASs to acquire new behaviors; most of these select exact Q-values in small discrete state space and action space. This article presents a joint Q-function linearly fuzzified for a MAS’ continuous state space, which overcomes the dimensionality problem. Also, this article gives a proof for the convergence and existence of the solution proposed by the algorithm presented. This article also discusses the numerical simulations and experimental results that were carried out to validate the proposed algorithm. View Full-Text
Keywords: multi-agent system (MAS); reinforcement learning (RL); mobile robots; function approximation multi-agent system (MAS); reinforcement learning (RL); mobile robots; function approximation
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Luviano-Cruz, D.; Garcia-Luna, F.; Pérez-Domínguez, L.; Gadi, S.K. Multi-Agent Reinforcement Learning Using Linear Fuzzy Model Applied to Cooperative Mobile Robots. Symmetry 2018, 10, 461.

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