# AlphaZero

> AlphaZero is DeepMind's self-taught AI that mastered chess, shogi, and Go through pure self-play reinforcement learning, revolutionizing game AI and broader AI research.

Source: https://metavert.io/alphazero  
Published: 2026-03-08  
Updated: 2026-03-11

**AlphaZero** is DeepMind's [AI](https://metavert.io/artificial-intelligence) system that taught itself to play [chess](https://metavert.io/chess-and-ai), shogi, and Go at superhuman levels through pure self-play [reinforcement learning](https://metavert.io/reinforcement-learning)—with no human game knowledge beyond the basic rules. Published in 2017, it remains one of the most significant demonstrations of AI's ability to discover knowledge from scratch.

AlphaZero was the direct successor to [AlphaGo](https://metavert.io/alphago) and AlphaGo Zero. Where AlphaGo first proved that deep learning could conquer Go — defeating world champion Lee Sedol in 2016 — and AlphaGo Zero showed this could be done without human game data, AlphaZero generalized the architecture to master multiple games simultaneously. The progression from [AlphaGo](https://metavert.io/alphago) to AlphaZero demonstrated that the self-play learning paradigm was domain-general: the same approach that conquered Go could conquer any well-defined strategic domain.

The achievement was stunning in its speed and originality. Starting from random play, AlphaZero trained for just nine hours on chess and reached a level that decisively defeated Stockfish, the world's strongest traditional chess engine. In a 100-game match, AlphaZero won 28 games and drew 72, losing none. More remarkable than the score was the style: AlphaZero played with a creative, almost human-like intuition—sacrificing material for long-term positional advantages, finding moves that top grandmasters described as beautiful and alien simultaneously.

AlphaZero's architecture combines a [deep neural network](https://metavert.io/neural-network) (which evaluates board positions and suggests promising moves) with Monte Carlo Tree Search (which explores future possibilities). The network is trained entirely through self-play: the system plays millions of games against itself, using the outcomes to refine its evaluation function. No opening books, no endgame tables, no human chess knowledge. This tabula rasa approach proved that superhuman performance could emerge from first principles and computation alone.

The broader implications extend far beyond games. AlphaZero demonstrated that reinforcement learning from self-play could discover strategies that centuries of human expertise had missed. This same principle—learning through trial and interaction rather than human-labeled examples—underlies [reinforcement fine-tuning](https://metavert.io/reinforcement-fine-tuning) of language models, [generative agents](https://metavert.io/generative-agents) that learn from environment interaction, and the emerging field of [AI-driven scientific discovery](https://metavert.io/ai-scientific-discovery) where models explore hypothesis spaces autonomously. AlphaZero was the proof of concept that self-directed AI learning could surpass human expertise.

## Related Topics

- [AlphaGo](https://metavert.io/alphago) — The predecessor that defeated Lee Sedol at Go
- [Chess & AI](https://metavert.io/chess-and-ai)
- [Reinforcement Learning](https://metavert.io/reinforcement-learning)
- [Game AI](https://metavert.io/game-ai)
- [Neural Network](https://metavert.io/neural-network)
- [AlphaFold](https://metavert.io/alphafold)
- [AI for Scientific Discovery](https://metavert.io/ai-scientific-discovery)
- [The Bitter Lesson](https://metavert.io/bitter-lesson)

## Further Reading

- [AlphaZero Chess Engine (Chess.com)](https://www.chess.com/terms/alphazero-chess-engine)
- [Chessmata: An Agentic Chess Platform, Built by Agents](https://meditations.metavert.io/p/chessmata-an-agentic-chess-platform)