Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis
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Human-level control through deep reinforcement learning presents a reinforcement learning approach for computer science.
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Utility signals: depth 65/100, grounding 58/100, status medium.
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Research context
30,356
Citations
38
References
Tasks
Computer science, Variety (cybernetics), Deep learning, Control (management), Perception, Human–computer interaction, Neuroscience, Cognitive Neuroscience
Methods
Reinforcement learning
Domains
Artificial intelligence, Machine learning
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