Aras 1.0.5 (CrazyAra, ClassicAra, MultiAra, XiangqiAra, StrategoAra))

Installation instructions

The default previous ClassicAra model is included within each release package.
Moreover, the binary packages include the required inference libraries for each platform.

The newer ClassicAra models can be downloaded in release 1.0.4.
You may choose alpha_vil_fx_models.zip and select a model size depending on your GPU/CPU and time-control.
At a very low time control (e.g. 30ms/Move), it is recommended to reduce the Batch-Size to 16.

The models for CrazyAra and MultiAra the models should be downloaded separately and unzipped (see release 0.9.5).

• CrazyAra-rl-model-os-96.zip
• MultiAra-rl-models.zip (improved MultiAra models using reinforcement learning (rl) )
• MultiAra-sl-models.zip (initial MultiAra models using supervised learning)

For XiangqiAra you can download XiangqiAra-sl-model.zip (see release 0.9.9).

Next, move the model files into the model/<engine-name>/<variant> folder.

Stratego is only included in the Linux release files as OpenSpiel is not officially supported on Windows and Mac.

Main changes

• Check for is_terminal() directly after creating a new node #204
• Virtual_Visit, Virtual_Mix, Virtual_Offset #205 (this led to ~100 Elo increase at very low node count / very fast TC)

Bug fixes

• Fix 960 initialization problem #207 (this affected CrazyAra version >= 0.9.5 and resulted in a ~30 Elo decrease)
• Fix first_and_second_max() #206

Regression test (from #205)

TC: 30ms/move
-each option.Batch_Size=16 option.Fixed_Movetime=30

Score of ClassicAra_1.0.5 vs ClassicAra-0.9.5: 526 - 243 - 231  [0.641] 1000
Elo difference: 101.1 +/- 19.4, LOS: 100.0 %, DrawRatio: 23.1 %

TC: 1min+0.1s game
-openings file=UHO_V3_8mvs_big_+140_+169.epd -each option.Batch_Size=16

Score of ClassicAra_1.0.5 vs ClassicAra-0.9.5
Elo difference: 6.27 +/-  23.28

Inference libraries

The following inference libraries are used in each package:

• Aras_1.0.5_Linux_TensorRT
  • TensorRT-8.2.3.0.Linux.x86_64-gnu.cuda-11.4.cudnn8.2
• Aras_1.0.5_Win_TensorRT
  • TensorRT-8.2.2.1.Windows10.x86_64.cuda-11.4.cudnn8.2
• Aras_1.0.5_Linux_OpenVino.zip
  • openvino_toolkit_ubuntu18_2023.0.1.11005
• Aras_1.0.5_Mac_OpenVino.zip
  • openvino_toolkit_macos_10_15_2023.0.1.11005
• Aras_1.0.5_Win_OpenVino.zip
  • openvino_toolkit_windows_2023.0.1.11005

Models - Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers

This release contains the different models used in the final comparision in our paper: Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers. Put the model files (.tar, .onnx) into the corresponding model directory (e.g. ./model/ClassicAra/chess/). Only the .onnx-files are used for inference. You can remove the .tar-files if you are not interested in reinforcement learning or fine tuning the model.

Update (2023-26-10)
For more exhaustive information regarding ..., please consult:

• network architectures: https://github.com/QueensGambit/CrazyAra/tree/a95a3b802208ca6269a05c50b52cc69969cdb16d/DeepCrazyhouse/src/domain/neural_net/architectures/pytorch
• input representations: https://github.com/QueensGambit/CrazyAra/blob/a95a3b802208ca6269a05c50b52cc69969cdb16d/DeepCrazyhouse/src/domain/variants/input_representation.py , https://github.com/QueensGambit/CrazyAra/blob/a95a3b802208ca6269a05c50b52cc69969cdb16d/engine/src/environments/chess_related/inputrepresentation.cpp#L627-L678
• value loss formulations: https://github.com/QueensGambit/CrazyAra/blob/a95a3b802208ca6269a05c50b52cc69969cdb16d/DeepCrazyhouse/src/domain/neural_net/architectures/pytorch/builder_util.py#L246-L301

Update: 2024-06-10: Fixed Batchnorm for alpha_vil_fx_models.zip and alpha_vil_model.zip

ClassicAra 1.0.3

This version has been submitted to the TCEC Season 23 event.

The engine.json configuration file and update.sh shell script can be used to replicate the testing environment on a multi-GPU Linux operating system.

Changelog

• Update MCTS solver for MCTS_SINGLE_PLAYER (#184)

• Dockerfile Pytorch Support (#183)

• Fen position from epd file (#182)

• Dynamic ONNX shape support (#181)

• Update RL-Loop (#180)

• Pytorch Deep Learning Backend (#179)

• Update binaryio.py (#178)

• Rename mctsmatch and evaltournament (#177)

(no improvement strength wise)

StrategoAra, Hex, DarkHex 1.0.2 (models only)

This release features the model files for BarrageStratego, Darkhex and Hex.

ClassicAra 1.0.1

This version has been submitted to the FRC 5 and DFRC 1 event.

The engine.json configuration file and update.sh shell script can be used to replicate the testing environment on a multi-GPU Linux operating system.

Changelog

• Tablebase Mating Sequences (#176)
• Rename mctsmatch and evaltournament (#177)
• Update binaryio.py (#178)
• Pytorch Deep Learning Backend (#179)
• Update RL-Loop (#180)

ClassicAra 1.0.0

This version has been submitted to the TCEC Cup 10 event.

The engine.json configuration file and update.sh shell script can be used to replicate the testing environment on a multi-GPU Linux operating system.

Changelog

• Fix terminal solver for MCTS_SINGLE_PLAYER #168

• Remove dependency of SF for non chess related environments #169

• fixed params file selection from the main_config for the neural_net_api.py #171

• Hex #172

• Remove Child_Threads and SearchThreadMaster #173

• Backend TensorRT 7 #174

Aras 0.9.9 (CrazyAra, ClassicAra, MultiAra, XiangqiAra)

Notes

Features

• First experimental XiangqiAra release.

  • Move generation back-end and Xiangqi ruleset is based on Fairy-Stockfish.
  • Uses supervised neural network trained on 10k human Xiangqi games.
    Please refer to the thesis Evaluation of Monte-Carlo Tree Search for Xiangqi by Maximilian Langer, pdf for more information.

• UCI_Chess_960 support as introduced in https://github.com/QueensGambit/CrazyAra/releases/tag/0.9.8. (However, no official 960 network yet.)

• TensorRT API Update #164

Major bug fixes

• Handle flooding of UCI-commands (#167)
  • CrazyAra Going To Infinite Analysis Mode On 1 Position (Can't Be Stopped) In Liground After Making Two Moves On The Board (#81)
• Avoid repeating positions in Xiangqi (closes #101) #166

TCEC

This version has been submitted to the TCEC Season 22.

ClassicAra 0.9.9 uses the wdlp-rise3.3-input3.0 model which was trained on the Kingbase2019lite data set as for release 0.9.5.

The engine.json configuration file and update.sh shell script can be used to replicate the testing environment on a multi-GPU Linux operating system.

Installation instructions

The latest ClassicAra model is included within each release package.
Moreover, the binary packages include the required inference libraries for each platform.

However, the models for CrazyAra and MultiAra the models should be downloaded separately and unzipped (see release 0.9.5).

• CrazyAra-rl-model-os-96.zip
• MultiAra-rl-models.zip (improved MultiAra models using reinforcement learning (rl) )
• MultiAra-sl-models.zip (initial MultiAra models using supervised learning)

For XiangqiAra you can download XiangqiAra-sl-model.zip (see release 0.9.9).

Next, move the model files into the model/<engine-name>/<variant> folder.

Inference libraries

The following inference libraries are used in each package:

• Aras_0.9.9_Linux_TensorRT
  • TensorRT-8.2.3.0.Linux.x86_64-gnu.cuda-11.4.cudnn8.2
• Aras_0.9.9_Win_TensorRT
  • TensorRT-8.0.1.6.Windows10.x86_64.cuda-11.3.cudnn8.2
• Aras_0.9.9_Linux_OpenVino.zip
  • OpenVino 2021.4.582 LTS
• Aras_0.9.9_Mac_OpenVino.zip
  • OpenVino 2021.4.582 LTS
• Aras_0.9.9_Win_OpenVino.zip
  • OpenVino 2021.4.582 LTS

Updates

2022-05-20: Aras_0.9.9_Win_OpenVino.zip: Fixed spelling of folder name: XinagqiAra -> XiangqiAra (thanks to @piladinmew for the hint)

ClassicAra 0.9.8

This version has been submitted to the TCEC FRC 4 event.
The option UCI_Chess960 has been added in ClassicAra 0.9.8 by default.

The engine.json configuration file and update.sh shell script can be used to replicate the testing environment on a multi-GPU Linux operating system.

Due to some difficulties in converting a newly trained network to ONNX, the same neural network model is used as in classical chess.

ClassicAra 0.9.7.post0

This version has been submitted to the TCEC Swiss 2 event.
ClassicAra 0.9.7 has a higher GPU and CPU utilization thanks to a higher batch size and more threads (#160).

The engine.json configuration file and update.sh shell script can be used to replicate the testing environment on a multi-GPU Linux operating system.

Regression test

• TC: 7s + 0.1s
• Opening suite: Unbalanced_Human_Openings_V3/UHO_V3_+150_+159/UHO_V3_8mvs_big_+140_+169.epd

Score of ClassicAra 0.9.7 (Threads 2, ChildThreads 4, BSize 64) vs ClassicAra 0.9.6 (Threads 2, BSize 16):
 64 - 26 - 72 [0.617]
Elo difference: 83.0 +/- 40.2, LOS: 100.0 %, DrawRatio: 44.4 %

162 of 1000 games finished.

• 0.9.7.post0: Deactivated removed get_avg_depth() implementation to avoid potential crash

Known issues

• TensorRT Memory Free Error (#161)

ClassicAra 0.9.6

This version has been submitted to the TCEC Cup 9 which starts on 17 October 2021, 17.00 UTC.
ClassicAra 0.9.6 uses the wdlp-rise3.3-input3.0 model which was trained on the Kingbase2019lite data set as for release 0.9.5.

The engine.json configuration file and update.sh shell script can be used to replicate the testing environment on a multi-GPU Linux operating system.

Notes

• The changes mainly include support for the latest TensorRT version, TensorRT-8.2.0.6. In terms of strength, it is very similar to release 0.9.5.post0.