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Methods › Reinforcement Learning › Board Game Models › AlphaZero

AlphaZero

114 papers tagged archive 2025-07-28

Introduced by David Silver et al. in Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

AlphaZero is a reinforcement learning agent for playing board games such as Go, chess, and shogi.

PaperSource

Papers archive 2025-07-28

30 shown of 114, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 65 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Board Games26
Reinforcement Learning (RL)26
reinforcement-learning22
Reinforcement Learning20
Decision Making15
Atari Games8
Deep Reinforcement Learning8
Game of Go8
Game of Chess6
Sequential Decision Making5
GPU4
Model-based Reinforcement Learning4
Math3
Q-Learning3
CPU2
Combinatorial Optimization2
GSM8K2
Game of Shogi2
Graph Neural Network2
Model Predictive Control2

Usage over time archive 2025-07-28

Papers per year tagged with AlphaZero: 2017 to 2025, peak 24 24 0 2017: 1 paper 2017 2018: 4 papers 2018 2019: 13 papers 2019 2020: 16 papers 2020 2021: 16 papers 2021 2022: 12 papers 2022 2023: 22 papers 2023 2024: 24 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (114 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Board Game Models

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