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LizGoban 0.8.0-pre5

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@kaorahi kaorahi released this 26 Jul 13:44
· 89 commits to master since this release
v0.8.0-pre5
62a1922
  • Upgrade KataGo to 1.15.1.
  • Separate the model files from *.exe for larger models including a model for human-like style.
  • Add "human-style" features.
    • Compare the policies between 5kyu and 1dan, for example.
    • Play human-like moves for the specified rank.
  • Warn overlooked high-policy best moves by squares on stones.
  • Avoid too stupid moves in persona strategy.
  • Add random pair match.
  • Deleted boards are also listed in "Open recent" menu.
  • Deprecate the display of preferred moves by "AIs for handicap games".
  • Deprecate homemade "aggressiveness" features and rely on the native KataGo features.

Incompatibilities:

  • Change the autosave format.
  • Upgrade libraries (Electron 31, etc.). So you may need to do "npm install" again if you use LizGoban from the command line.

Human-style features

Choose "Human-like Analysis" or "Human-like Play" from "Preset" menu and refer to "KataGo" section in "Help" menu for details.

Thanks to dfannius; this analysis feature is a variation of his "policy heatmap".

To use it on 64bit Windows immediately

Just download the all-in-one package (LizGoban-*_win_*.zip), unzip it, and double-click LizGoban *.exe. You do not need installation, configuration, additional downloads, and so on. Its file size is due to the built-in engine:

You can switch KataGo versions (CPU and GPU) by [Preset] menu in LizGoban. The first run of the GPU version may take a long time (1 hour on a low-spec machine, for example) for its initial tuning. You can also choose "Human-like Analysis" or "Human-like Play" from [Preset] menu. Refer to "KataGo" section in [Help] menu for details.

To customize it on 64bit Windows

If you want to use another network (aka. model, weights), you can simply click the Engine menu and select "Load network weights". Additionally, you can modify the config.json file for more flexible configuration. See README for details.

To use it on other platforms (Mac, Linux, ...)

Download the source code and see README.md.

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