Learning to Play Hearthstone Using Machine Learning
نویسندگان
چکیده
The subject of this thesis is a new game called Hearthstone. It is a strategy card game developed by Blizzard Entertainment, in which players duel with each other with cards they collected. The game of Hearthstone provides a challenge for developing an artificial intelligence (AI) agent. The agent has to be able to deal with unknown information and stochastic events in a large search space. In this thesis four different strategies are explored to create such an AI agent for playing the game of Hearthstone; a simple random bot, a rulebased bot, a Monte Carlo bot and a Monte Carlo Tree Search bot are implemented and compared with each other.
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