Simulating Human Game Play for Level Difficulty Estimation with Convolutional Neural Networks
This thesis presents an approach to predict the difficulty of levels in a game by simulating game play following a policy learned from human game play. Using state-action pairs tracked from players of the game Candy Crush Saga, we train a Convolutional Neural Network to predict an action given a gam...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för informations- och kommunikationsteknik (ICT)
2017
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215699 |