KataGo

KataGo's public distributed training run is ongoing! See for more details, to download the latest and strongest neural nets, or to learn how to contribute if you want to help KataGo improve further! Also check out the computer Go discord channel! Experimentally, KataGo did also try some limited ways of using external data at the end of its June 2020 run, and has continued to do so into its most recent public distributed run, "kata1" at External data is not necessary for reaching top levels of play, but still appears to provide some mild benefits against some opponents, and noticeable benefits in a useful analysis tool for a variety of kinds of situations that don't occur in self-play but that do occur in human games and games that users wish to analyze. KataGo's engine aims to be a useful tool for Go players and developers, and supports the following features: Estimates territory and score, rather than only "winrate", helping analyze kyu and amateur dan games besides only on moves that actually would swing the game outcome at pro/superhuman-levels of play. Cares about maximizing score, enabling strong play in handicap games when far behind, and reducing slack play in the endgame when winning. Supports alternative values of komi (including integer values) and good high-handicap game play. Supports board sizes ranging from 7x7 to 19x19, and as of May 2020 may be the strongest open-source bot on both 9x9 and 13x13 as well. Supports a wide variety of rules, including rules that match Japanese rules in almost all common cases, and ancient stone-counting-like rules. For tool/back-end developers - supports a JSON-based analysis engine that can batch multiple-game evaluations efficiently and be easier to use than GTP. Paper about the major new ideas and techniques used in KataGo: Accelerating Self-Play Learning in Go (arXiv). Many of the specific parameters are outdated, but the general methods continue to be used. Many major further improvements have been found since then, which have been incorporated into KataGo's more recent runs and are documented here: KataGoMethods.md. Many thanks to Jane Street for supporting the training of KataGo's major earlier published runs, as well as numerous many smaller testing runs and experiments. Blog posts about the initial release and some interesting subsequent experiments: Accelerating Self-Play Learning in Go Deep-Learning the Hardest Go Problem in the World. Also if you're looking to ask about general information about KataGo or how it works, or about some past Go bots besides KataGo, consider the computer Go discord channel. Where To Download Stuff Precompiled executables for KataGo can be found at the releases page for Windows and Linux. Setting Up and Running KataGo KataGo implements just a GTP engine, which is a simple text protocol that Go software uses. It does NOT have a graphical interface on its own. So generally, you will want to use KataGo along with a GUI or analysis program. A few of them bundle KataGo in their download so that you can get everything from one place rather than downloading separately and managing the file paths and commands. KaTrain - KaTrain might be the easiest to set up for non-technical users, offering an all-in-one package (no need to download KataGo separately!), modified-strength bots for weaker players, and good analysis features. LizzieYzy Next or LizzieYzy - Various more recent forks of Lizzie, with a lot more features and reworked interfaces but still focusing on live interactive review. Lizzie - Used to be very popular for running long interactive analyses and visualizing them as they happen. Note that Lizzie at this point is a bit old and, e.g. the version of KataGo packaged with it is very outdated, and there are many long-standing interface bugs in Lizzie itself that have never been fixed. Nowadays, one of the several forks of Lizzie might be preferable. Ogatak is a KataGo-specific GUI with an emphasis on displaying the basics in a snappy, r