---
title: 'Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/1712-01815-alphazero.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: David Silver and 12 coauthors (listed on the arXiv abs page), Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: AI-for-science'
tags: arxiv, reinforcement-learning, self-play, games
arxiv_id: 1712.01815
alias: 1712.01815-alphazero
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm'
    publisher: arXiv
    authors: 'David Silver and 12 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/1712.01815
    version_read: 'v1 (5 Dec 2017)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

## Summary
[Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://arxiv.org/abs/1712.01815) (2017) is the AlphaZero paper. It showed that one general reinforcement-learning algorithm, trained by self-play, could master chess, shogi, and Go, without game-specific hand-crafted evaluation functions of the older kind.

What it introduced: a single self-play algorithm, combining a neural network with Monte-Carlo tree search, that reached superhuman play in all three games from a common setup.

What it led to: a landmark for scientific and games RL, and a template later used in other search-and-self-play systems. For later generalist policies in robotics rather than board games, see the Open X-Embodiment verification note in batch B.

It is the usual citation when someone asks whether a single self-play algorithm can master more than one perfect-information game. Where to read it: https://arxiv.org/abs/1712.01815 (v1, 5 Dec 2017).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

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