Hello chess engine enthusiasts,
we are happy to announce that the first version of MultiAra is now available.
MultiAra is a version of the Ara project which supports all chess variants available on lichess.org (antichess, atomic, chess960, crazyhouse, king-of-the-hill, horde, racing kings and three-check).
The neural network models have been initialized by lichess.org game data from August 2013 until July 2020.
All variant (except chess960) have been improved using reinforcement learning.
Details can be found in the master thesis by Maximilian Alexander Gehrke which among others covers an ablation study between training from zero knowledge and supervised initialization, as well as a preliminary strength comparison against Fairy-Stockfish:
Assessing Popular Chess Variants Using Deep Reinforcement Learning (https://ml-research.github.io/papers/ge ... essing.pdf).
ClassicAra
The model for ClassicAra uses a new input representation (https://github.com/QueensGambit/CrazyAra/pull/134) and a new WDLP value output head (https://github.com/QueensGambit/CrazyAra/pull/123).
The old model and input representation for chess is still supported as the input representation version is now inferred by the model name.
More information and binaries for Linux, Windows, Mac for GPU and CPU can be found in the official release notes:
CrazyAra, ClassicAra, MultiAra 0.9.5 release
Moderators: hgm, Rebel, chrisw
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- Full name: Johannes Czech
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Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
Thanks for this. Do you have a sense of playing strength versus the previous release?
Fat Titz by Stockfish, the engine with the bodaciously big net. Remember: size matters. If you want to learn more about this engine just google for "Fat Titz".
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- Full name: Johannes Czech
Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
Yes, the new input representation and WDLP head resulted in a 44.83 +/- 20.54 Elo increase.
The neural network was trained on the same data set using the same training configuration.
Most of the strength increase is attributed to the new input representation.
ClassicAra
Code: Select all
tc=0/1:0+0.25
-openings file="UHO_V3_6mvs_+090_+099.pgn"
ClassicAra-0.9.5-rise_3.3-input_3.0-wldp vs ClassicAra-0.9.4-rise_3.3-input_1.0:
202 - 125 - 273 (WLD)
Elo difference: 44.83 +/- 20.54
CrazyAra
The crazyhouse version, CrazyAra, was only affected by a small speed increase and dynamically loading the neural network input shape.
Code: Select all
tc=0/1:0+0.25
-openings file="crazyhouse_mix_cp_130.epd"
CrazyAra-0.9.5 vs CrazyAra-0.9.4
119 - 112 - 369 (WLD)
Elo difference: 4.05 +/- 17.24
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- Full name: Dietrich Kappe
Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
I’ve used lichess data for training Bad Gyal. One of the challenges I faced is that agreed draws and resignations meant I didn’t have enough endgame and imbalanced positions. To that end, I played all the kingbase 2300+ FIDE games out to terminal with sf10 100k nodes. Made a huge difference in the training.
The data can be found here: https://github.com/dkappe/leela-chess-w ... -Gyal-Data
The data can be found here: https://github.com/dkappe/leela-chess-w ... -Gyal-Data
Fat Titz by Stockfish, the engine with the bodaciously big net. Remember: size matters. If you want to learn more about this engine just google for "Fat Titz".
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Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
Okay, I started a LTC game with new version of Slowchess 2.7
[d]rnbq1rk1/ppp1bppp/4pn2/8/2pP4/1PN1PN2/P4PPP/R1BQKB1R w KQ - 0 1
Both engines are thinking now.
[d]rnbq1rk1/ppp1bppp/4pn2/8/2pP4/1PN1PN2/P4PPP/R1BQKB1R w KQ - 0 1
Both engines are thinking now.
"Good decisions come from experience, and experience comes from bad decisions."
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Ted Summers
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Ted Summers
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Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
18 moves in ...
[d]2rr3k/4qpp1/p3pb2/1pn5/8/1P3P2/PBB2P1P/RQ1R2K1 w - - 0 1
[d]2rr3k/4qpp1/p3pb2/1pn5/8/1P3P2/PBB2P1P/RQ1R2K1 w - - 0 1
"Good decisions come from experience, and experience comes from bad decisions."
__________________________________________________________________
Ted Summers
__________________________________________________________________
Ted Summers
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Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
Thanks for the new release.IQ_QI wrote: ↑Thu Aug 26, 2021 2:25 pm Hello chess engine enthusiasts,
we are happy to announce that the first version of MultiAra is now available.
MultiAra is a version of the Ara project which supports all chess variants available on lichess.org (antichess, atomic, chess960, crazyhouse, king-of-the-hill, horde, racing kings and three-check).
The neural network models have been initialized by lichess.org game data from August 2013 until July 2020.
All variant (except chess960) have been improved using reinforcement learning.
Details can be found in the master thesis by Maximilian Alexander Gehrke which among others covers an ablation study between training from zero knowledge and supervised initialization, as well as a preliminary strength comparison against Fairy-Stockfish:
Assessing Popular Chess Variants Using Deep Reinforcement Learning (https://ml-research.github.io/papers/ge ... essing.pdf).
ClassicAra
The model for ClassicAra uses a new input representation (https://github.com/QueensGambit/CrazyAra/pull/134) and a new WDLP value output head (https://github.com/QueensGambit/CrazyAra/pull/123).
The old model and input representation for chess is still supported as the input representation version is now inferred by the model name.
More information and binaries for Linux, Windows, Mac for GPU and CPU can be found in the official release notes:
The previous versions work well on my i7, but the new one just gives a nondescript error and will not run.
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Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
Did you remember to to 1st run it from command line and type isready. The 1st run will take a while, but after that it should be fine. You only have to do this once.carldaman wrote: ↑Sun Aug 29, 2021 8:00 amThanks for the new release.IQ_QI wrote: ↑Thu Aug 26, 2021 2:25 pm Hello chess engine enthusiasts,
we are happy to announce that the first version of MultiAra is now available.
MultiAra is a version of the Ara project which supports all chess variants available on lichess.org (antichess, atomic, chess960, crazyhouse, king-of-the-hill, horde, racing kings and three-check).
The neural network models have been initialized by lichess.org game data from August 2013 until July 2020.
All variant (except chess960) have been improved using reinforcement learning.
Details can be found in the master thesis by Maximilian Alexander Gehrke which among others covers an ablation study between training from zero knowledge and supervised initialization, as well as a preliminary strength comparison against Fairy-Stockfish:
Assessing Popular Chess Variants Using Deep Reinforcement Learning (https://ml-research.github.io/papers/ge ... essing.pdf).
ClassicAra
The model for ClassicAra uses a new input representation (https://github.com/QueensGambit/CrazyAra/pull/134) and a new WDLP value output head (https://github.com/QueensGambit/CrazyAra/pull/123).
The old model and input representation for chess is still supported as the input representation version is now inferred by the model name.
More information and binaries for Linux, Windows, Mac for GPU and CPU can be found in the official release notes:
The previous versions work well on my i7, but the new one just gives a nondescript error and will not run.
"Good decisions come from experience, and experience comes from bad decisions."
__________________________________________________________________
Ted Summers
__________________________________________________________________
Ted Summers
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Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
It opens up when run from the command line, but immediately crashes after isready.
Did not have to do any of this with the previous versions.
Did not have to do any of this with the previous versions.
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- Location: Munich
Re: CrazyAra, ClassicAra, MultiAra 0.9.5 release
That's how it was for me too, because I had overlooked it:
"The binary packages include the required inference libraries for each platform.
However, the models should be downloaded separately and unzipped.
CrazyAra-rl-model-os-96.zip
ClassicAra-sl-model-wdlp-rise3.3-input3.0.zip
MultiAra-rl-models.zip (improved MultiAra models using reinforcement learning (rl) )
MultiAra-sl-models.zip (initial MultiAra models using supervised learning)
Next, move the model files into the model/<engine-name>/<variant> folder."