Introduction

This repository contains tool for playing go with AI feedback aimed at kyu players. The idea is to give immediate feedback on the many large mistakes we make in terms of inefficient moves. It is based on the KataGo AI and relies heavily on score estimation rather than win rate.

Some uses include:

• Analyze your games to find the moves that were most costly in terms of points lost.
• Play against AI and get immediate feedback on mistakes with option to retry.
• Play against a stronger player and use the retry option instead of handicap stones.
• Play a match with an evenly matched friend where both players get instant feedback.

Manual

Installation for Windows users

• Make sure you have a python installation, I will assume Anaconda (Python 3.7) https://www.anaconda.com/distribution/#download-section
• Open 'Anaconda prompt'
• Execute the command 'pip install kivy'
• Start the app by running python katrain.py in the directory where you downloaded the scripts.

Installation for linux/Mac users

• This assumed you have a working Python 3.6+ installation, use pip3/python3 etc. if your default is python 2.
• pip install kivy
• Change the engine.command field in config.json to your kata v1.3+ binary. Compiled binaries and source code can be found at https://github.com/lightvector/KataGo/releases. Executables for Mac are not available, so compiling from source code is required there.
• Start the app by running python katrain.py

Options

• Check box options

  • Eval: show the coloured dots on the moves for this player.
  • Hints: show suggested moves for this player.
  • Undo: automatically undo poor moves for this player and make them try again.
  • AI: let the AI control this player. Check both for self-play.
  • Show owner: show expected control of territory.
  • 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.
  • Fast: use a lower number of max visits for evaluation/AI move.
  • Balance score: Deliberately make sub-optimal moves as the AI in an attempt to balance the score towawrds a slight win.

• Temperature/Evaluation/Score: Not that these fields can be hidden by clicking on the text.

  • Temperature is the point difference between passing and the best move.
  • Evaluation is where on this scale the last move was, from 0% (equivalent to a pass) to 100% (best move). 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.
  • Score: How far one player is ahead.

Play

• Play against the AI

  • Turn on AI for the chosen player.
  • Choose whether to turn on balance score to make the AI play slack moves.
  • Choose whether to turn on undo for your colour to be prompted to re-try poor moves.
  • 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).
  • Possibly lock AI to prevent yourself from peeking at hints, etc.
  • Possibly hide score or temperature.
  • If you chose AI to play black, click AI move for the first move.

• Engine-assisted play

  • Turn off auto move.
  • Choose whether to turn on undo for either colour to be prompted to re-try poor moves.
  • Possibly lock AI to prevent peeking at hints.
  • Possibly hide score or temperature.
  • 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)

• Analysis

  • Copy the SGF into the text box. Note that branches are not supported and will lead to strange results.
  • Choose whether or not to turn on fast to make the AI weaker but analyze faster.
  • Click Load
  • Alternatively click Load when the text box is empty to get a file chooser dialog.

• Save game

  • Click save to get an sgf with comments saved in the sgfout/ directory (and a short version in the text box).

Configuration

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). You can use python katrain.py your_config_file.json to use another config file instead.

The trainer block has the following options to tweak:

• balance_play_target_score: indicates how many points the AI aims to win by when using 'balance score'.
• 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.
• balance_play_min_eval: when needing to balance score, the AI will pick a move which is at least this good.
• balance_play_min_visits: never pick a move with fewer playouts than this.
• undo_eval_threshold, undo_point_threshold: prompt player to undo if move is worse than this in terms of points AND evaluation.
• num_undo_prompts: automatically undo bad moves when undo is on at most this many times.

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).

Contributing

• Feedback and pull requests are both very welcome.
• For suggestions and planned improvements, see the 'issues' tab on github.