Historical milestone · 2016
AlphaGo
Reviewed through September 18, 2026
2016 · Historical milestone
AlphaGo
- Era
- 2010s
- Theme
- Agent planning & cognitive architectures
- Evidence form
- Peer-reviewed system experiment
- School / paradigm
- Search + reinforcement learning
- Institution / context
- Google DeepMind
- Researchers
- David Silver; collaborators
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Combined supervised policy learning, reinforcement learning, value estimation, and Monte Carlo tree search.
Result / historical claim. Defeated the European Go champion 5 to 0 and showed how learned evaluation can guide classical search.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: Agent planning and cognitive architectures.
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Peer-reviewed system experiment
Limitation / debate. Go is closed, deterministic, and fully observable; the result does not establish reliable planning in open worlds.
Source status. The source link below is the verified link our reviewed topic research already carries for this milestone.
Reproduce
A reproduction tutorial is linked only when one exists for this exact record.
A reproduction tutorial is not yet available for this entry. The closest reviewed material is Agent planning and cognitive architectures.
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.