Training Multiagent Systems by Q‐Learning: Approaches and Empirical Results. (4th March 2014)

• Record Type: Journal Article
• Authors: Lopez‐Guede, Jose Manuel; Fernandez‐Gauna,, Borja; Graña, Manuel; Zulueta, Ekaitz
• Is Part Of: Computational intelligence. Volume 31:Number 3(2015:Aug.)
• Page Start: 498
• Page End: 512
• Publication Date: 2014-03-04
• Subjects: Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12035
• Languages: English
• ISSNs: 0824-7935

Abstract

<abstract abstract-type="main" id="coin12035-abs-0001"> <title>Abstract</title> <p id="coin12035-para-0001">Multiagent systems are increasingly present in computational environments. However, the problem of agent design or control is an open research field. Reinforcement learning approaches offer solutions that allow autonomous learning with minimal supervision. The Q‐learning algorithm is a model‐free reinforcement learning solution that has proven its usefulness in single‐agent domains; however, it suffers from dimensionality curse when applied to multiagent systems. In this article, we discuss two approaches, namely TRQ‐learning and distributed Q‐learning, that overcome the limitations of Q‐learning offering feasible solutions. We test these approaches in two separate domains. The first is the control of a hose by a team of robots. The second is the trash disposal problem. Computational results show the effectiveness of Q‐learning solutions to multiagent systems' control.</p> </abstract>