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

Overview of attention for article published in Nature, January 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 48,832)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Readers on

mendeley
5011 Mendeley
citeulike
25 CiteULike
Title
Mastering the game of Go with deep neural networks and tree search
Published in
Nature, January 2016
DOI 10.1038/nature16961
Pubmed ID
Authors

David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis, Silver, David, Huang, Aja, Maddison, Chris J, Guez, Arthur, Sifre, Laurent, van den Driessche, George, Schrittwieser, Julian, Antonoglou, Ioannis, Panneershelvam, Veda, Lanctot, Marc, Dieleman, Sander, Grewe, Dominik, Nham, John, Kalchbrenner, Nal, Sutskever, Ilya, Lillicrap, Timothy, Leach, Madeleine, Kavukcuoglu, Koray, Graepel, Thore, Hassabis, Demis, Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 1,902 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 5,011 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 123 2%
United Kingdom 62 1%
Germany 43 <1%
Japan 34 <1%
China 28 <1%
Spain 17 <1%
Netherlands 15 <1%
France 13 <1%
Canada 13 <1%
Other 129 3%
Unknown 4534 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1501 30%
Student > Master 1045 21%
Researcher 863 17%
Student > Bachelor 591 12%
Other 259 5%
Other 752 15%
Readers by discipline Count As %
Computer Science 2440 49%
Engineering 778 16%
Agricultural and Biological Sciences 338 7%
Physics and Astronomy 332 7%
Mathematics 162 3%
Other 961 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3158. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2017.
All research outputs
#94
of 8,652,770 outputs
Outputs from Nature
#27
of 48,832 outputs
Outputs of similar age
#7
of 336,298 outputs
Outputs of similar age from Nature
#3
of 966 outputs
Altmetric has tracked 8,652,770 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 48,832 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 76.0. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 336,298 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 966 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.