GoGoGo: Improving Deep Neural Network Based Go Playing AI with Residual Networks

Introduction

Xingyu Liu

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Input (19x19x5)

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Output

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P

Fig. 2 (a) Policy Network

CONV3, 64

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Input (19x19x5)

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(b) Value Network

Network Architecture Experiment Result

• Go playing AIs using Traditional Search:

GNU Go, Pachi, Fuego, Zen etc.

Training Methodology and Data

• From Kifu to Input Feature Maps

Channels: 1) Space Positions; 2) Black Positions; 3)

White Positions; 4) Current Player; 5) Ko Positions

[1] David Silver et al., “Mastering the game of go with deep neural networks and

tree search”, Nature, 529:484–503, 2016.

[2] Kaiming He et al, “Deep residual learning for image recognition”, CoRR,

abs/1512.03385, 2015.

SL on Policy

Network

RL on Policy

Network

RL on Value

Network

• Dynamic Board State Expansion

Ko fight performing. Saves disk space. Small Mem

• Use Ing Chang-ki rule

Board State + Ko is Game State, No need to

remember the number of captured stones

• Two Levels of Batches (Kifus, moves)

Random Shuffling. Mem usage small and locality.

• Powered by Deep Learning:

Zen → Deep Zen Go, darkforest, AlphaGo

• Goal: From by Vanilla CNN to ResNets

CONV3

Batch Norm

ReLU

CONV3

Batch Norm

ReLU

Data

Eltwise Add

(c) Residual Module

FC23104 1

Hyperparameters Value

Base learning rate 2E-4

Decay Policy Exp

Decay Rate 0.95

Decay Step (kifu) 200

Loss Function Softmax

(b) Hyperparameters

Future Work Fig 1. Ko fight explicity

expansion

• Training Accuracy ~ 32%

• Testing Accuracy ~ 26%

Fig. 3 GoGoGo plays against itself, policy network only

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Supervised Learning Training Loss

/100 batches

• Monte Carlo Tree Search

𝑎𝑡 = argmax𝑎

(𝑄 𝑠𝑡 , 𝑎 + 𝑢(𝑠𝑡, 𝑎))

𝑢(𝑠, 𝑎) ∝𝑃(𝑠, 𝑎)

1 + 𝑁(𝑠, 𝑎)

• Reinforcement Learning of Value Network

• Network Architecture Exploration

• Real Match Testing against Human Players