Game of Chess

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Most implemented papers archive 2025-07-28

7 shown of 7 papers with code (24 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
  5 Dec 2017 62 repositories listed Syntology ran 13 of 17 samples · 4 unverified · 9 pointer-only (licence)
  The game of chess is the most widely-studied domain in the history of artificial intelligence.
• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
  19 Nov 2019 18 repositories listed Syntology ran 43 of 64 samples · 21 unverified · 62 pointer-only (licence)
  When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.
• Giraffe: Using Deep Reinforcement Learning to Play Chess
  4 Sep 2015 3 repositories listed
  This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer.
• Chess as a Testbed for Language Model State Tracking
  26 Feb 2021 2 repositories listed
  Motivated by this issue, we consider the task of language modeling for the game of chess.
• DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
  27 Nov 2017 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  We present an end-to-end learning method for chess, relying on deep neural networks.
• Playing Chess with Limited Look Ahead
  4 Jul 2020 1 repository listed
  We have seen numerous machine learning methods tackle the game of chess over the years.
• Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data
  1 Jul 2018 1 repository listed
  This paper examines the problem of generating natural language descriptions of chess games.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.