Polygames: Improved Zero Learning
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Abstract (arXiv)
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connected layer). Using such an architecture plus global pooling, we can create bots independent of the board size. The training can be made more robust by keeping track of the best checkpoints during the training and by training against them. Using these features, we release Polygames, our framework for Zero learning, with its library of games and its checkpoints. We won against strong humans at the game of Hex in 19x19, which was often said to be untractable for zero learning; and in Havannah. We also won several first places at the TAAI competitions.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.
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Citing papers per year: 2021: 5; 2022: 3; 2023: 3.
11 papers.
- 2021Scaling Scaling Laws with Board Gamescited by 62
- 2023MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Gamescited by 14
- 2021Deep Learning for General Game Playing with Ludii and Polygamescited by 7
- 2021Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variantscited by 6
- 2021Batch Monte Carlo Tree Searchcited by 5
- 2021Improving Model and Search for Computer Gocited by 3
- 2022Spatial State-Action Features for General Gamescited by 3
- 2022AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Timecited by 3
- 2022Impartial Games: A Challenge for Reinforcement Learningcited by 3
- 2023Vision Transformers for Computer Gocited by 2
- 2023Learning to Play Stochastic Two-player Perfect-Information Games without Knowledgecited by 1
- 2023MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games17 Oct 2023
- 2023Vision Transformers for Computer Go22 Sep 2023
- 2023Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge8 Feb 2023
- 2022Impartial Games: A Challenge for Reinforcement Learning25 May 2022
- 2022AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time28 Apr 2022
- 2022Spatial State-Action Features for General Games17 Jan 2022
- 2021Batch Monte Carlo Tree Search9 Apr 2021
- 2021Scaling Scaling Laws with Board Games7 Apr 2021
- 2021Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants24 Feb 2021
- 2021Improving Model and Search for Computer Go6 Feb 2021
- 2021Deep Learning for General Game Playing with Ludii and Polygames23 Jan 2021