Neural Foundations · 2016

Mastering the Game of Go (AlphaGo/AlphaZero)

David Silver, Aja Huang, Chris J. Maddison, Julian Schrittwieser, Thomas Hubert, Demis Hassabis

Introduced AlphaGo and later AlphaZero, systems that combined deep neural networks with Monte Carlo tree search to master Go and other board games, with AlphaZero learning entirely from self-play with no human game data.

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Plain-language summary

AlphaGo paired a policy network (which moves to consider) and a value network (who is winning) with tree search to defeat top human Go players. AlphaZero simplified this into a single network trained purely by playing against itself, guided by search, and generalized to chess and shogi. The work showed that self-play plus search could reach superhuman strength without human examples or handcrafted evaluation.

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Record ID
A-034
Record created
2026-07-13
Last reviewed
2026-07-14
Record version
2

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