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dc.contributor.authorNeo, Yong Taien_US
dc.identifier.citationNeo, Y. T. (2022). Learning multi-agent competitive games with reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractReinforcement Learning has been applied and has had promising results in various fields. For example, in the field of games which includes AI learning different on the board games, to video games like Starcraft and Dota. All of these examples involves the training of multi agents that either cooperate, compete or mixed. This brings the importance of learning about MARL. Problems that are more practical usually involves the need to make use of MARL. In the field of competitive games, there will be for example, to have a simulation that is closer to the real world if the AI that is interacting with the player is more intelligent. This will make it usable and appealing for solving real world problems. This is what makes learning about multi-agent competitive games appealing. In this paper, I describe and show what I have learnt by setting up a 3v3 game of soccer using the ML-Agents toolkit and comparing the algorithms that is available.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleLearning multi-agent competitive games with reinforcement learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLana Obraztsovaen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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