Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch
Typ
Examensarbete på kandidatnivå
Program
Publicerad
2019
Författare
BENNHAGE, SVANTE
GULDBRAND, ERIC
OUEIDAT, OMAR
TORSTENSSON, MATTIAS
ULANDER, SILAS
WALLHEDE, ERIK
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Artificial 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 network.
Beskrivning
Ämne/nyckelord
Artificial Neural Networks , Machine Learning , StarCraft II , Reinforcement learning , Supervised learning