Neural Foundations · 2016
Mastering the Game of Go (AlphaGo/AlphaZero)
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.
Editorial record
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.
Knowledge graph
Relationships
Antecedents
Conceptual ancestorEvidence: Strongly supported
Highly Accurate Protein Structure Prediction with AlphaFold
Continues DeepMind line of deep learning for hard structured problems
book
Descendants
ExtendsEvidence: Strongly supported
TD-Gammon
Self-play lineage from TD-Gammon to AlphaGo
A-034
EnablesEvidence: Strongly supported
The Monte Carlo Method / Metropolis Algorithm
Monte Carlo Tree Search in AlphaGo
A-034
Source record
Provenance
- Record ID
- A-034
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
Citation caveat: Citation metadata is approximate and marked unverified in the source dataset.