Volodymyr Mnih,
a Canadian research scientist at at GoogleDeepMind with expertise in deep learning, heading the team working on deep Q-networks (DQN) mastering Atari games[1]. DQNs were tested with games such as Pong, Space Invaders, Breakout and Seaquest, receiving only the pixels and the game score as inputs, to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. Volodymyr Mnih holds a Ph.D. in machine learning from University of Toronto under supervision of Geoffrey E. Hinton, and a Master's degree in computing science fro University of Alberta where his advisor was Csaba Szepesvári.
a Canadian research scientist at at Google DeepMind with expertise in deep learning, heading the team working on deep Q-networks (DQN) mastering Atari games [1]. DQNs were tested with games such as Pong, Space Invaders, Breakout and Seaquest, receiving only the pixels and the game score as inputs, to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. Volodymyr Mnih holds a Ph.D. in machine learning from University of Toronto under supervision of Geoffrey E. Hinton, and a Master's degree in computing science fro University of Alberta where his advisor was Csaba Szepesvári.
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[3]2008
2010 ...
2015 ...
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