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Reinforcement learning based AI to play first-person video games

Vaneberck, Damien ; De Graeve, Quentin
(2021)

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DeGraeve_90841400_Vaneberck_77931400_2021.pdf
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Details

Supervisors
Schaus, Pierre
Faculty
Ecole polytechnique de Louvain
Ecole polytechnique de Louvain
Degree label
Master [120] en sciences informatiques, à finalité spécialisée
Master [120] : ingénieur civil en informatique, à finalité spécialisée
Abstract
In our current world, artificial intelligence and machine learning is constantly getting more and more importance in a variety of domains, in order to get autonomous vehicles or smarter programs where the human intelligence is not sufficient. One domain of interest is reinforcement learning, which aims to get a program to discover and learn his environment, in order to accomplish a given task without direct supervision from a human. This master thesis explores reinforcement learning and more specifically, Q-learning. The goal will be to create an agent which is going to learn to play video games autonomously. We believe that using games can be a good playground to train programs safely before taking them to the real world to perform more useful tasks, such as exploring difficult terrain or piloting self-driving cars. The paper will present in detail how the algorithms work and which techniques can make it perform better.