  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.
