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Learning for RoboCup Soccer: Policy Gradient Reinforcement Learning inmulti-agent systems
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]

Robo Cup Soccer is a long-running yearly world wide robotics competition,in which teams of autonomous robot agents play soccer against each other.This report focuses on the 2D simulator variant, where no actual robots are needed and the agents instead communicate with a server which keeps trackof the game state. RoboCup Soccer 2D simulation has become a major topic of research for articial intelligence, cooperative behaviour in multi-agent systems, and the learning thereof. Some form of machine learning is mandatory if you want to compete at the highest level, as the problem is too complex for manualconguration of a teams decision making.This report nds that PGRL is a common method for machine learning in Robo Cup teams, it is utilized in some of the best teams in Robo Cup. The report also nds that PGRL is an effective form of machine learning interms of learning speed, but there are many factors which affects this. Most often a compromise have to made between speed of learning and precision.

Abstract [sv]

Robo Cup Soccer är en årlig världsomspännande robotiktävling, i vilken lag av autonoma robotagenter spelar fotboll mot varandra. Denna rapport fokuserar på 2D-simulatorn, vilken är en variant där inga riktiga robotar behövs, utan där spelarklienterna istället kommunicerar med en server vilken håller reda på speltillståndet. RoboCup Soccer 2D simulation har blivit ett stort ämne för forskning inom articiell intelligens, samarbete och beteende i multi-agent-system, och lärandet därav. Någon form av maskininlärning är ett krav om man villkunna tävla på den högsta nivån, då problemet är för komplext för att beslutsfattandet ska kunna programmeras manuellt.Denna rapport finner att PGRL är en vanlig metod för maskininlärning i Robo Cup-lag, den används inom några av de bästa lagen i Robo Cup. Rapporten nner också att PGRL är en effektiv form av maskininlärningn är det gäller inlärningshastighet, men att det finns många faktorer som kan påverka detta. Oftast måste en avvägning ske mellan inlärningshastighet och precision.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157469OAI: oai:DiVA.org:kth-157469DiVA: diva2:769994
Examiners
Available from: 2015-05-28 Created: 2014-12-09 Last updated: 2015-05-28Bibliographically approved

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CiteExportLink to record
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