TamaGo

TamaGo is a Go (Weiqi, Baduk) engine implemented in Python. TamaGo has following features,

• Supervised learning using SGF-format files.
• Reinforcement learning with Gumbel AlphaZero method.
• Move generation using Monte-Carlo tree search and a trained neural network model.

TamaGo runs on Python 3.6 or higher.

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• TamaGo
• Requirements
• Installation
• How to execute GTP engine
  • Examples of TamaGo execution as GTP engine.
  • Trained neural network parameters file.
• How to execute supervised learning
• How to execute reinforcement learning
• GoGui analyze commands
• Analyze commands
• CGOS analyze mode
• License

Requirements

Package name	Purpose
click	Implementation of command line options
numpy	Fast calculation
pytorch	Implementation of neural network construction and learning

Installation

You can install TamaGo by executing the following command in a Python-installed computer.

pip install -r requirements.txt

You can use TamaGo with CPU only. It is recommended that you set up your computer so that you can use a GPU when executing neural network learning.

How to execute GTP engine

When using a GUI that supports GTP (Go Text Protocol), the following command canb be used TamaGo as a engine,

python main.py

TamaGo's command line options are as follows,

Option	Description	Value	Example of value	Default value	Note
--size	Size of go board	Integer number more than 1 and less than or equal to BOARD_SIZE	9	BOARD_SIZE	BOARD_SIZE is defined in board/constant.py.
--superko	Activation super-ko rule	true or false	true	false	It supports only positional super-ko.
--model	Path to a trained neural network parameters file	String of model file path	model/model.bin	None	It must be relative path from TamaGo's home directory
--use-gpu	Flag to use a GPU	true or false	true	false
--policy-move	Flag to move according to Policy distribution	true or false	true	false
--sequential-halving	Flag to use SHOT (Sequential Halving applied to trees) for searching	true or false	true	false	It's for debugging
--visits	The number of visits per move	Integer number more than 0	1000	1000	When you use '--const-time' or '--time' options, this option is ignored.
--const-time	Time to thinking per move	Real number more than 0	10.0	None	When you use '--const-time' or '--time' options, this option is ignored.
--time	Total remaining time for a game	Real number more than 0	600.0	None
--batch-size	Mini-batch size for MCTS	Integer number more than 0	13	NN_BATCH_SIZE	NN_BATCH_SIZE is defined in mcts/constant.py.
--tree-size	Maximum number of MCTS nodes	Integer number more than 0	100000	MCTS_TREE_SIZE	MCTS_TREE_SIZE is defined in mcts/constant.py.
--cgos-mode	Enable to capture all dead stones.	true or false	true	false

Examples of TamaGo execution as GTP engine.

1. Setting board size to 5, using model/model.bin as a trained file, avoiding to use a GPU.

python main.py --size 5 --model model/model.bin --use-gpu false

2. Activating super-ko rule.

python main.py --superko true

3. Using model/sl-model.bin as a trained file, generating moves with Policy distribution.

python main.py --model model/sl-model.bin --policy-move true

4. Setting total remaining time to 10 minites.

python main.py --time 600

5. Setting the number of visits per move to 500.

python main.py --visits 500

6. Setting time to thinking time per move to 10 seconds.

python main.py --const-time 10.0

7. Settings for testing on CGOS.

python main.py --model model/sl-model.bin --use-gpu true --cgos-mode true --superko true --batch-size 13 --time 600 --komi 7 --tree-size 200000

Trained neural network parameters file.

Trained neural network parameters file is available here. When you place a trained file in the "model" direcotry under the name "model.bin", you can run TamaGo using a trained file without a command line option. If TamaGo's structure of neural network and a trained neural network parameter files are unmached, TamaGo cannot load a trained file. Please care about version of the trained model file and version of TamaGo. On TamaGo 0.6.3 is pretty stronger than GNUGo level 10 (about +420 elo). Using Monte Carlo tree search, TamaGo 0.6.3 (100 visits/move) with sl-model.bin is stronger than Ray ver 9.0 10k playouts/move (about +180 elo).
TamaGo has changed it's neural network structure from version 0.6.0, You cannot use trained model files for older version.

How to execute supervised learning

For more information on how to execute supervised learning, please check here.

How to execute reinforcement learning

For more information on how to execute reinforcement learning, please check here.

GoGui analyze commands

When you use GoGui, you can check Policy values for current position, and color according to Policy values.
Value range of Policy is more than or equal 0.0 and less than or equal to 1.0.

Redder is higher value, bluer is lower value.

Analyze commands

TamaGo version 0.7.0 supports lz-analyze and lz-genmove_analyze commands. When you use Lizzie or Sabaki, you can analyze positions using these commands.

CGOS analyze mode

TamaGo version 0.7.0 supports cgos-analyze, cgos-genmove_analyze commands. When you connect Computer Go Server (CGOS) for testing TamaGo, you can check search information enabling --cgos-mode option.

License

You can use TamaGo under Apache License 2.0.