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Training Multi-Agent Collaboration using Deep Reinforcement Learning in Game Environment / Träning av sambarbete mellan flera agenter i spelmiljö med hjälp av djup förstärkningsinlärning

Deep Reinforcement Learning (DRL) is a new research area, which integrates deep neural networks into reinforcement learning algorithms. It is revolutionizing the field of AI with high performance in the traditional challenges, such as natural language processing, computer vision etc. The current deep reinforcement learning algorithms enable an end to end learning that utilizes deep neural networks to produce effective actions in complex environments from high dimensional sensory observations, such as raw images. The applications of deep reinforcement learning algorithms are remarkable. For example, the performance of trained agent playing Atari video games is comparable, or even superior to a human player. Current studies mostly focus on training single agent and its interaction with dynamic environments. However, in order to cope with complex real-world scenarios, it is necessary to look into multiple interacting agents and their collaborations on certain tasks. This thesis studies the state-of-the-art deep reinforcement learning algorithms and techniques. Through the experiments conducted in several 2D and 3D game scenarios, we investigate how DRL models can be adapted to train multiple agents cooperating with one another, by communications and physical navigations, and achieving their individual goals on complex tasks. / Djup förstärkningsinlärning (DRL) är en ny forskningsdomän som integrerar djupa neurala nätverk i inlärningsalgoritmer. Det har revolutionerat AI-fältet och skapat höga förväntningar på att lösa de traditionella problemen inom AI-forskningen. I detta examensarbete genomförs en grundlig studie av state-of-the-art inom DRL-algoritmer och DRL-tekniker. Genom experiment med flera 2D- och 3D-spelscenarion så undersöks hur agenter kan samarbeta med varandra och nå sina mål genom kommunikation och fysisk navigering.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-240316
Date January 2018
CreatorsDeng, Jie
PublisherKTH, Skolan för elektroteknik och datavetenskap (EECS)
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationTRITA-EECS-EX ; 2018:779

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