Browsing Faculty of Engineering and Science by Subject "Q-Learning"
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Towards a deep reinforcement learning approach for Tower Line Wars
(Lecture Notes in Artificial Intelligence, Journal article; Peer reviewed, 2017)There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is ...