alpha-zero-boosted
Rating-Correlations
| alpha-zero-boosted | Rating-Correlations | |
|---|---|---|
| 2 | 3 | |
| 79 | 1 | |
| - | - | |
| 3.2 | 3.3 | |
| almost 4 years ago | about 1 year ago | |
| Python | Python | |
| - | Apache License 2.0 |
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alpha-zero-boosted
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DeepMind has open-sourced the heart of AlphaGo and AlphaZero
> I came up with a nifty implementation in Python that outperforms the naive impl by 30x, allowing a pure python MCTS/NN interop implementation. See https://www.moderndescartes.com/essays/deep_dive_mcts/
Great post!
Chasing pointers in the MCTS tree is definitely a slow approach. Although typically there are < 900 "considerations" per move for alphazero. I've found getting value/policy predictions from a neural network (or GBDT[1]) for the node expansions during those considerations is at least an order of magnitude slower than the MCTS tree-hopping logic.
[1] https://github.com/cgreer/alpha-zero-boosted
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MuZero: Mastering Go, chess, shogi and Atari without rules
What you can do is checkout the algorithm at a particular stages of development. AlphaZero&Friends start out not being very good at the game, then over time they learn and become super human. You typically checkpoint the weights for the model at various stages. So early on, the algo would be like a 600 elo player for chess and then eventually get to superhuman elo levels. So if you wanted to train you can gradually play against versions of the algo until you can beat them by loading up the weights at various difficulty stages.
I implemented AlphaZero (but not Mu yet) using GBDTs instead of NNs here if you're curious about how it would work: https://github.com/cgreer/alpha-zero-boosted. Instead of saving the "weights" for a GBDT, you save the splitpoints for the value/policy models, but the concept is the same.
Rating-Correlations
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Contribute to chessprogramming.org
maybe ask ferdy from chess stackexchange aka fsmosca from github aka Ferdinand Mosca who has a chessprogramming.org page here?
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the lichess rating correlation web app is done! (ratingcorrelations.herokuapp.com) unlike chessratingcomparison.com, it allows multiple inputs and has outputs for chess960 and crazyhouse!
shared on github! https://github.com/fsmosca/Rating-Correlations/issues/3
This appears to have been created by a github user named fsmosca aka Philippine mechanical engineer Ferdinand Mosca. See here for rating correlations.
What are some alternatives?
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mlforecast - Scalable machine 🤖 learning for time series forecasting.
leela-zero - Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper.
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.