Developing a Self-learning Intelligent Agent in StarCraft II - Deep Reinforcement Learning with Imitation Learning
Typ
Examensarbete på kandidatnivå
Program
Publicerad
2019
Författare
Chiu Falck, Karl-Rehan
Johansson, Niclas
Svensson, Emma
Veintie, Markus
Wang, Franz
Willim, Daniel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Knowledge of machine learning is becoming more essential in many fields. This
thesis explores and outlines the basics of machine learning through the complex
game StarCraft II with limited prior knowledge and resources. In particular deep
Q-learning in combination with imitation learning was explored in order to reduce
the time required for an agent to become capable of playing the game.
A few simpler environments were used as initial challenges before StarCraft II
was explored. For all environments, the thesis reports a comparison of performance
between the agents utilizing imitation learning and those that did not. In the cases
of the simpler environments, agents using deep Q-learning combined with imitation
learning showed significantly improved training time. Due to problems with the
reward structure for the complex game StarCraft II no conclusion could be drawn
about the implications of imitation learning in complex environments.
Beskrivning
Ämne/nyckelord
Deep Q-Learning , Deep Q-Network , Imitation Learning , Machine Learning , PySC2 , Reinforcement Learning , StarCraft II