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Mastering the game of Go with deep neural networks and tree search


The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

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Figure 1: Neural network training pipeline and architecture.
Figure 2: Strength and accuracy of policy and value networks.
Figure 3: Monte Carlo tree search in AlphaGo.
Figure 4: Tournament evaluation of AlphaGo.
Figure 5: How AlphaGo (black, to play) selected its move in an informal game against Fan Hui.
Figure 6: Games from the match between AlphaGo and the European champion, Fan Hui.

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We thank Fan Hui for agreeing to play against AlphaGo; T. Manning for refereeing the match; R. Munos and T. Schaul for helpful discussions and advice; A. Cain and M. Cant for work on the visuals; P. Dayan, G. Wayne, D. Kumaran, D. Purves, H. van Hasselt, A. Barreto and G. Ostrovski for reviewing the paper; and the rest of the DeepMind team for their support, ideas and encouragement.

Author information

Authors and Affiliations



A.H., G.v.d.D., J.S., I.A., M.La., A.G., T.G. and D.S. designed and implemented the search in AlphaGo. C.J.M., A.G., L.S., A.H., I.A., V.P., S.D., D.G., N.K., I.S., K.K. and D.S. designed and trained the neural networks in AlphaGo. J.S., J.N., A.H. and D.S. designed and implemented the evaluation framework for AlphaGo. D.S., M.Le., T.L., T.G., K.K. and D.H. managed and advised on the project. D.S., T.G., A.G. and D.H. wrote the paper.

Corresponding authors

Correspondence to David Silver or Demis Hassabis.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Table 1 Details of match between AlphaGo and Fan Hui
Extended Data Table 2 Input features for neural networks
Extended Data Table 3 Supervised learning results for the policy network
Extended Data Table 4 Input features for rollout and tree policy
Extended Data Table 5 Parameters used by AlphaGo
Extended Data Table 6 Results of a tournament between different Go programs
Extended Data Table 7 Results of a tournament between different variants of AlphaGo
Extended Data Table 8 Results of a tournament between AlphaGo and distributed AlphaGo, testing scalability with hardware
Extended Data Table 9 Cross-table of win rates in per cent between programs
Extended Data Table 10 Cross-table of win rates in per cent between programs in the single-machine scalability study
Extended Data Table 11 Cross-table of win rates in per cent between programs in the distributed scalability study

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Supplementary information

Supplementary Information

This zipped file contains game records for the 5 formal match games played between AlphaGo and Fan Hui. (ZIP 3 kb)

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Silver, D., Huang, A., Maddison, C. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

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