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

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@QueensGambit QueensGambit released this 08 Aug 22:07
· 27 commits to master since this release
1.0.5
b566c04

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
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