# Playing Go without Game Tree Search Using Convolutional Neural Networks

## TL;DR

This paper presents a convolutional neural network that plays Go at a high level without using traditional game tree search, leveraging novel structures and training methods to mimic human intuition.

## Contribution

It introduces non-rectangular convolutions, supervised learning on professional games, and reinforcement learning to develop a strong Go-playing neural network without tree search.

## Key findings

- Surpassed intermediate amateur skill level with supervised learning.
- Introduced non-rectangular convolutions for better shape learning.
- Further training is expected to improve performance significantly.

## Abstract

The game of Go has a long history in East Asian countries, but the field of Computer Go has yet to catch up to humans until the past couple of years. While the rules of Go are simple, the strategy and combinatorics of the game are immensely complex. Even within the past couple of years, new programs that rely on neural networks to evaluate board positions still explore many orders of magnitude more board positions per second than a professional can. We attempt to mimic human intuition in the game by creating a convolutional neural policy network which, without any sort of tree search, should play the game at or above the level of most humans. We introduce three structures and training methods that aim to create a strong Go player: non-rectangular convolutions, which will better learn the shapes on the board, supervised learning, training on a data set of 53,000 professional games, and reinforcement learning, training on games played between different versions of the network. Our network has already surpassed the skill level of intermediate amateurs simply using supervised learning. Further training and implementation of non-rectangular convolutions and reinforcement learning will likely increase this skill level much further.