126 lines
7.5 KiB
Markdown
126 lines
7.5 KiB
Markdown
Introduction
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============
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This repository contains tool for playing go with AI feedback.
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The idea is to give immediate feedback on the many large mistakes we make in terms of inefficient moves.
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It is based on the KataGo AI and relies heavily on score estimation rather than win rate.
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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 a stronger player and use the retry option instead of handicap stones.
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* Play a match with an evenly matched friend where both players get instant feedback.
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Manual
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======
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Installation for Windows users
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------------------------------
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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.
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* Execute the command 'pip install 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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--------------------------------
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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 kivy
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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+ 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 your download 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 kata's gpu tuning.
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Options
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-------
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* Check box options
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* All Eval: show the coloured dots on all the moves for this player.
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* Hints: show suggested moves for this player and output more statistics on moves.
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* Undo: automatically undo poor moves for this player and make them try again.
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* AI: let the AI control this player. Check both for self-play.
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* Show owner: show expected control of territory.
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* Lock AI: disallow extra undos, changing hints options, changing auto move, or AI move. Also turns off the option to click on a move to see detailed comments.
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* Fast: use a lower number of max visits for evaluation/AI move.
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* Balance score: Deliberately make sub-optimal moves as the AI in an attempt to balance the score towards a slight win.
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* Temperature/Evaluation/Score: Not that these fields can be hidden by clicking on the text.
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* Temperature is the point difference between passing and the best move.
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* Evaluation is where on this scale the last move was, from 0% (equivalent to a pass) to 100% (best move).
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This can be < 0% in case of suicidal moves, or >100% when Kata did not consider the move before, or further analysis shows it to be better than the best one considered.
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* Score: How far one player is ahead.
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* Keyboard controls
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* Arrow up: undo
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* Arrow down: redo
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* Arrow left/right: alternate branch.
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Play
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----
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* Play against the AI
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* Turn on AI for the chosen player.
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* Choose whether to turn on `balance score` to make the AI play slack moves.
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* Choose whether to turn on `undo` for your colour to be prompted to re-try poor moves.
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* Choose whether or not to turn on `fast` to make the AI play faster but read less deeply (NB: with balance score, faster AI can be a stronger opponent, as there are fewer mediocre moves considered).
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* Possibly lock AI to prevent yourself from peeking at hints, etc.
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* Possibly hide score or temperature.
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* If you chose AI to play black, click AI move for the first move.
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* Engine-assisted play
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* Turn off auto move.
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* Choose whether to turn on `undo` for either colour to be prompted to re-try poor moves.
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* Possibly lock AI to prevent peeking at hints.
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* Possibly hide score or temperature.
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* Play with a friend with instant feedback and/or undos for both, or see how many stones stronger you are with one undo. (But please play unranked and be honest to your opponent on what you're doing)
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* Analysis
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* Click 'Load' when the text box is empty-ish to get a file chooser dialog. Note that branches are not supported and will lead to strange results.
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* Select if you want fast analysis or rewinding to the start for reviewing. Note that the 'fast' checkbox still affects speed as well,
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this is just an additional lowering of visits.
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* Alternatively copy the SGF into the text box and click 'Load'.
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* Save game
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* Click save to get an sgf with comments saved in the sgfout/ directory (and a short version in the text box).
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Configuration
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-------------
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`config.json` has a number of options, many of them are stylistic, but also including the command kata is started with (and so the kata config and model).
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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 `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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* `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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* `dont_lock_undos`: don't lock the undo button when `ai lock` is active.
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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 kata. In particular, it changes the default to being more exploratory and score-based (and therefore nicer as an opponent, but weaker as analysis tool).
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FAQ
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===
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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 `analysis` block of `config.json` by half or so and try again.
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* Why are the dots changing colour?
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* If the next move made is the predicted top move, more information is available to analyze the previous move and this is used to update the evaluation.
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* Can I play on sizes other than 9, 13 or 19?
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* Type in `SZ[n]HA[h]KM[k]` in the text box and hit 'load' for a game on a n by n board with h handicap stones and k komi, but note that the default KataGo does not support sizes above 19x19.
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Contributing
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============
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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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