Shusaku

Go IA.

Dependencies

libgoban

Introduction

The goal of this project is to build an AI Agent for the game of Go based on AlphaGo Zero. The main idea is to reduce as most as possible the computation power needed and the computation time for that kind of agent by for example reducing the neural network architecture.

Methodology

Main differences between AlphaGo Zero and LittleGo

The neural network architecture consists in a "tower" of residual convolutionnal blocks where each block has 2 convolutionnnal layers and a residual connection connecting the input of the block. Then the network is separated in two heads, one for the prediction of move and the other for the prediction of the game outcome (evaluation function).

	AlphaGoZero	LittleGo
Number of residual blocks	20 or 40	5
Number of convolutional filters	256	64
Input planes	17	5
Optimizer	RMSProp	Adam
Number of parameters	24,019,048 (20 blocks) 47,642,728 (40 blocks)	7,517,931

Experiments and Results

Supervised Learning

In order to validate their architecture DeepMind trains the neural network with supervised learning on the KGS dataset (https://u-go.net/gamerecords/) for training and testing set and GoKifu dataset (http://gokifu.com/) for validation set. Due to our computation limitation we were, for the moment, only able to train on a subset of the KGS dataset.

	KGS dataset size
AlphaGo Zero	~ 30,000,000
AlphaGo	~ 30,000,000
LittleGo	75,000
LittleGo first_version	25,000

Below, the results tables for move prediction accuracy and game outcome prediction error, the important value to analyze is "KGS test".

Move prediction accuracy

	KGS train	KGS test	GoKifu validation
AlphaGo Zero (20 block)	62.0	60.4	54.3
AlphaGo (12 layer)	59.1	55.9	-
LittleGo	62.9	38.6	-
LittleGo first_version	43.8	37.0	-

Game outcome prediction error

	KGS train	KGS test	GoKifu validation
AlphaGo Zero (20 block)	0.177	0.185	0.207
AlphaGo (12 layer)	0.19	0.37	-
LittleGo	0.15	0.195	-
LittleGo first_version	0.27	0.26	-

Reinforcement Learning

Move prediction accuracy

	KGS train	KGS test	GoKifu validation
AlphaGo Zero (20 block)	-	-	49.0
AlphaGo Zero (40 block)	-	-	51.3

Game outcome prediction error

	KGS train	KGS test	GoKifu validation
AlphaGo Zero (20 block)	-	-	0.177
AlphaGo Zero (40 block)	-	-	0.180