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Informatics colloquium 5. 12. Mastering games with deep learningInformatics colloquium 5. 12. 2017, 14:00 lecture hall D2 doc. RNDr. Tomáš Brázdil, Ph.D., FI MU Mastering games with deep learning Abstract: The main theme of the talk is how much a deep learning agent could learn by playing games against itself. I will start with a historical overview of TD-Gammon, a famous algorithm for playing backgammon at expert level. In the main part I will concentrate on the very recent algorithms for playing Go above master level. I will give an overview of fundamental methods, namely deep reinforcement learning and Monte Carlo tree search. Finally, I will comment on new directions in the area, especially the Starcraft II challenge in which the current algorithms still fail to deliver convincing results.