105 lines
5.9 KiB
Markdown
105 lines
5.9 KiB
Markdown
# KaTrain v1.0
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This repository contains tool for analyzing and playing go with AI feedback from KataGo.
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The original idea was to give immediate feedback on the many large mistakes we make in terms of inefficient moves,
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but has since grown to include a wide range of features.
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Some uses include:
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* Review your games to find the moves that were most costly in terms of points lost.
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* Play against AI and get immediate feedback on mistakes with option to retry.
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* Play against weakened versions of AI.
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* Play against a stronger player and use the retry option instead of handicap stones.
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## Installation
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### Installation for Windows users
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* Download the repository by clicking the green *Clone or download* on this page and *Download zip*. Extract the contents.
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* Make sure you have a python installation, I will assume Anaconda (Python 3.7), available [here](https://www.anaconda.com/distribution/#download-section).
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* Open 'Anaconda prompt' from the start menu and navigate to where you extracted the zip file using the `cd <folder>` command.
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* Execute the command `pip install numpy kivy_deps.glew kivy_deps.sdl2 kivy_deps.gstreamer kivy`
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* Start the app by running `python katrain.py` in the directory where you downloaded the scripts. Note that the program can be slow to initialize the first time, due to kata's gpu tuning.
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### Installation for Linux/Mac users
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* This assumed you have a working Python 3.6/3.7 installation as a default. If your default is python 2, use pip3/python3. Kivy currently does not have a release for Python 3.8.
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* Git clone or download the repository.
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* `pip install -U kivy numpy`
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* Put your KataGo binary in the `KataGo/` directory or change the `engine.command` field in `config.json` to your KataGo v1.3.5+ binary.
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* Compiled binaries and source code can be found [here](https://github.com/lightvector/KataGo/releases).
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* You will need to `chmod +x katago` your binary if you downloaded it.
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* Executables for Mac are not available, so compiling from source code is required there.
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* Start the app by running `python katrain.py`. Note that the program can be slow to initialize the first time, due to KataGo's GPU tuning.
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## Manual
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### Play
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* Human is simple play with potential feedback, but without auto-undo.
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* Teach will give you instant feedback, and auto-undo bad moves to give you a second chance.
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* AI will active the AI in the dropdown next to the buttons:
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* Default is full KataGo, above professional level.
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* Balance is KataGo occasionally making weaker moves, attempting to win by ~2 points.
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* Jigo is KataGo aggressively making weaker moves, attempting to win by 0.5 points.
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* Policy is the top move from the policy network (it's 'shape sense' without reading), should be around high dan level depending on the model used.
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* P+Pick will pick a `pick_n + pick_frac * <number of legal moves>` moves at random, and play the best move among them.
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The setting `pick_override` determines the minimum value at which this process is bypassed to play the best move instead, preventing obvious blunders.
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This is probably the best choice for kyu players who want a chance of winning. Variants of this strategy include:
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* P+Local will pick such moves biased towards the last move with probability related to `local_stddev`.
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* P+Tenuki is biased in the opposite way as P+Local, using the same setting.
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* P+Influence is biased towards 4th+ line moves, with every line below that dividing both the chance of considering the move and the policy value by `influence_weight`. Consider setting `pick_frac=1.0` to only affect the policy weight.
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* P+Territory is biased in the opposite way, towards 1-3rd line moves, using the same setting.
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* P+Noise mixes the policy with `noise_strength` Dirichlet noise. At `noise_strength=0.9` play is near-random, while `noise_strength=0.7` is still quite strong. Regardless, mistakes are typically strange can include senseless first-line moves.
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* `<Pause>` pauses AI moves, in case you want to do analysis without triggering moves, or simply hide the evaluation dots for this player.
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### Analysis
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### Keyboard shortcuts
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### Configuration
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Configuration is stored in `config.json`. Most settings are available to edit in the program, but some are not.
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You can use `python katrain.py your_config_file.json` to use another config file instead.
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#### The settings panel
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The `trainer` block has the following options to tweak for engine assisted play and reviewing:
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* `eval_off_show_last`: when the `eval` checkbox is off for a player, show coloured dots on the last this many moves regardless.
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* `undo_eval_threshold`, `undo_point_threshold`: prompt player to undo if move is worse than this in terms of points AND evaluation.
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The following options are relevant for the `balance score` AI play mode.
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* `balance_play_target_score`: indicates how many points the AI aims to win by when using 'balance score'.
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* `balance_play_randomize_eval`: when not needing to balance score, the AI will pick a random move which is at least this good as long as it stays ahead.
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* `balance_play_min_eval`: when needing to balance score, the AI will pick a move which is at least this good.
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* `balance_play_min_visits`: never pick a move with fewer playouts than this.
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The cfg file has additional configuration for KataGo, which are documented there.
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#### Configuring feedback
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* `num_undo_prompts`: automatically undo bad moves when `undo` is on at most this many times. Can be a fraction like 0.5 for 50% chance of being granted an undo on a bad move.
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## FAQ
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* The program is slow to start!
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* The first startup of KataGo can be slow, after that it should be much faster.
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* The program is running too slowly!
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* Lower the visits count in the `max_visits` block of `config.json` by half or so and try again.
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## Contributing
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* Feedback and pull requests are both very welcome.
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* For suggestions and planned improvements, see the 'issues' tab on github.
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