3 5 4 | N A T U R E | V O L 5 5 0 | 1 9 O c T O b E R 2 0 1 7 ARTicLE doi:10.1038/nature24270 Mastering the game of Go without human knowledge David Silver1*, Julian Schrittwieser1*, Karen Simonyan1*, ioannis Antonoglou1, Aja Huang1, Arthur Guez1, Thomas Hubert1, Lucas baker1, Matthew Lai1, Adrian bolton1, Yutian chen1, Timothy Lillicrap1, Fan Hui1, Laurent Sifre1, George van den Driessche1, Thore Graepel1 & Demis Hassabis1 Much progress towards artificial intelligence has been made using supervised learning systems that are trained to replicate the decisions of human experts1–4. However, expert data sets are often expensive, unreliable or simply unavailable. Even when reliable data sets are available, they may impose a ceiling on the performance of systems trained in this manner5. By contrast, reinforcement learning systems are trained from their own experience, in principle allowing them to exceed human capabilities, and to operate in domains where
