Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch

dc.contributor.authorBENNHAGE, SVANTE
dc.contributor.authorGULDBRAND, ERIC
dc.contributor.authorOUEIDAT, OMAR
dc.contributor.authorTORSTENSSON, MATTIAS
dc.contributor.authorULANDER, SILAS
dc.contributor.authorWALLHEDE, ERIK
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerAhrendt, Wolfgang
dc.contributor.examinerFjeld, Morten
dc.contributor.examinerKnutsson, Sven
dc.contributor.supervisorDuregård, Jonas
dc.description.abstractArtificial intelligence research is currently a hot topic within many industries. In terms of research, games such as StarCraft II provide a good testing ground due to its accessibility. However, getting started can still be more difficult than it should be. This paper aims to facilitate the development of a machine learning agent for StarCraft II by designing tools for data collection, making a simple API built on top of PySC2 to facilitate interaction with the game and by analyzing a few different types of artificial neural networks with respect to StarCraft II. It is concluded that defining reward functions for reinforcement learning can give rise to unexpected behaviors. A further conclusion is that convolutional neural networks tend to be more resource intensive than non-convolutional networks and that they are thus less suited for anyone without access to large computational power. Lastly, a network is trained on collected data to continuously predict the win chance for players in a StarCraft II match. Unfortunately the network does not become successful in its task, likely in part due to the simplicity of the
dc.subjectArtificial Neural Networkssv
dc.subjectMachine Learningsv
dc.subjectStarCraft IIsv
dc.subjectReinforcement learningsv
dc.subjectSupervised learningsv
dc.titleMachine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratchsv
dc.type.degreeExamensarbete på kandidatnivåsv
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