Clarke, Danijel Charles

Kifle, Yohannes Kidane

Larsen, Emil Ellegaard

Thulstrup, Frederik Zeilberger

This project explores the subfield of machine learning, reinforcement learning which is definedas training an artificial intelligence with the use of rewards and punishments. Specifically wechose to investigate the reinforcement based algorithm Q-learning which and can be appliedto any problem where you can define the rewards and punishments in relation to actionstaken.We use Q-learning on a small game, to teach an agent how to play it. The agent is theplayer which is controlled by the Q-learning algorithm. Then we tested the agent in differentsettings on eight different maps. We varied the training both by quality, amount and ondifferent levels. This gives us an idea of how the program handles certain situations. Theresults led us to the conclusion that the amount of training, did not matter much, but thequality of the training did. When the amount of training was high and the quality bad, theresults seemed somewhat random.The final conclusion was that we did not have the balance between the amount and qualityof the training needed to get clear results, but is definitely worth investigating further

An investigation of an artificial intelligence trained with Q-learning solving different game levels

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Roskilde Universitet