REINFORCEMENT LEARNING FOR DECISION-MAKING IN A BUSINESS SIMULATOR

Javier García (), Fernando Borrajo and Fernando Fernández
Additional contact information
Javier García: Computer Science Department, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain
Fernando Borrajo: Faculty of Economics and Business Administration, Universidad Autónoma de Madrid, Ctra. de Colmenar Viejo, Km 15, 28049 Madrid, Spain
Fernando Fernández: Computer Science Department, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain

International Journal of Information Technology & Decision Making (IJITDM), 2012, vol. 11, issue 05, 935-960

Abstract: Business simulators are powerful tools for both supporting the decision-making process of business managers as well as for business education. An example is SIMBA (SIMulator for Business Administration), a powerful simulator which is currently used as a web-based platform for business education in different institutions. In this paper, we propose the application of reinforcement learning (RL) for the creation of intelligent agents that can manage virtual companies in SIMBA. This application is not trivial, given the particular intrinsic characteristics of SIMBA: it is a generalized domain where hundreds of parameters modify the domain behavior; it is a multi-agent domain where both cooperation and competition among different agents can coexist; it is required to set dozens of continuous decision variables for a given business decision, which is made only after the study of hundreds of continuous variables. We will demonstrate empirically that all these challenges can be overcome through the use of RL, showing results for different learning scenarios.

Keywords: Reinforcement learning; business simulator; multi-agent learning; competitive learning (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1142/S0219622012500277

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