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Multi-agent system with Policy Gradient Reinforcement Learning for RoboCup Soccer Simulator
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The RoboCup Soccer Simulator is a multi-agent soccer simulator used in competitions to simulate soccer playing robots. These competitionsare mainly held to promote robotics and AI research by providing a cheap and accessible way to program robot-like agents. In this report alearning multi-agent soccer team is implemented, described and tested.Policy Gradient Reinforcement Learning (PGRL) is used to train and alter the strategical decision making of the agents. The results show that PGRL improves the performance of the learningteam. But when the gap in performance between the learning team and the opponent is big the results were inconclusive.

Abstract [sv]

RoboCup Soccer Simulator är en multiagent fotbollssimulator som används i tävlingar för att simulera robotar som spelar fotboll. Dessa tävlingar hålls huvudsakligen för att marknadsföra forskning inom robotik och articiell intelligens genom att tillhandahålla ett billigt och lättillgängligt sätt att programmera robotlika agenter. I denna rapportbeskrivs och testas en implementation av ett multiagentfotbollslag. PolicyGradiend Reinforcement Learning (PGRL) används för att träna ochförändra lagets beteende. Resultaten visar att PGRL förbättrar lagets prestanda, men närlagets prestanda skiljer sig avsevärt från motståndarens blir resultatetofullständigt.3

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157418OAI: oai:DiVA.org:kth-157418DiVA: diva2:769983
Examiners
Available from: 2014-12-10 Created: 2014-12-09 Last updated: 2015-08-27Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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