lightvector/KataGo

GTP engine and self-play learning in Go

What it solves

KataGo is a high-performance open-source Go bot designed to be one of the strongest available. It solves the problem of providing a superhuman-level AI for the game of Go that can be trained from scratch using self-play, without requiring massive external datasets, and can be used for both high-level play and detailed game analysis.

How it works

KataGo uses an AlphaZero-like process for training, utilizing neural networks (including recent support for transformer models) and Monte-Carlo Graph Search (an extension of MCTS). It is designed for efficiency, allowing it to reach high amateur dan strength on a 19x19 board using only a few GPUs for a few days, or superhuman strength on a single consumer GPU over several months. It supports multiple hardware backends including OpenCL, CUDA, TensorRT, ROCm, ONNX Runtime, and Eigen (CPU).

Who it’s for

  • Go players who want a strong opponent or a tool for game analysis.
  • Developers whoต้องการ a GTP-compatible engine for Go software.
  • AI researchers interested in self-play training and Monte-Carlo Graph Search.

Highlights

  • Score and Territory Estimation: Unlike bots that only provide win rates, KataGo estimates territory and score, making it useful for analyzing amateur games.
  • Broad Support: Supports board sizes from 7x7 to 19x19 and a wide variety of rulesets.
  • Hardware Flexibility: Offers six different backends to ensure compatibility across NVIDIA, AMD, Intel GPUs, and CPUs.
  • Efficient Training: Capable of training from scratch to superhuman levels using relatively modest hardware.
  • Human-style Play: Can imitate human play at various ranks using a specific human SL model.

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