deepmind

Under review as a conference paper at ICLR 2015

MOVE EVALUATION IN GO USING DEEP

CONVOLUTIONAL NEURAL NETWORKS

Chris J. Maddison

University of Toronto

cmaddis@cs.toronto.edu

Aja Huang1, Ilya Sutskever2, David Silver1

Google DeepMind1, Google Brain2

{ajahuang,ilyasu,davidsilver}@google.com

ABSTRACT

The game of Go is more challenging than other board games, due to the difficulty

of constructing a position or move evaluation function. In this paper we investi-

gate whether deep convolutional networks can be used to directly represent and

learn this knowledge. We train a large 12-layer convolutional neural network by

supervised learning from a database of human professional games. The network

correctly predicts the expert move in 55% of positions, equalling the accuracy

of a 6 dan human player. When the trained convolutional network was used di-

rectly to play games of Go, without any search, it beat the traditional-search pro-

gram GnuGo in 97% of games, and matched the performance of a state-of-the-art

Monte-Carlo tree search that simulates two million positions per move.

INTRODUCTION

The most frequently cited reason for the difficulty of Go, compared to games such as Chess, Scrabble

or Shogi, is the difficulty of constructing an evaluation function that can differentiate good moves

from bad in a given position. The combination of an enormous state space of 10170 positions,

combined with sharp tactics that lead to steep non-linearities in the optimal value function, has led

many researchers to conclude that representing and learning such a function is impossible (M¨uller,

2002). In previous years, the most successful methods have sidestepped this problem altogether

using Monte-Carlo search, which dynamically evaluates a position through random sequences of

self-play. Such programs have led to strong amateur level performance, but a considerable gap still

remains between top professional players and the strongest computer programs. The majority of

recent progress has been due to increased quantity and quality of prior knowledge, which is used to

bias the search towards more promising states in both the search tree and during rollouts (Coulom,

2007; Gelly & Silver, 2011; Enzenberger et al., 2010; Huang et al., 2011), and it is widely believed

that this knowledge is the major bottleneck towards further progress (Huang & M¨uller, 2013). How-

ever, this knowledge again is ultimately compiled into an evaluation function or distribution that

expresses a preference over moves.

In this paper we address these fundamental questions of representation and learning of Go knowl-

edge, by using a deep convolutional neural network (CNN). Although CNNs have previously been

applied to the game of Go, with modest success (Schraudolph et al., 1994; Enzenberger, 1996;

Sutskever & Nair, 2008), previous architectures have typically been limited to one hidden layer of

relatively small size, and have not exploited recent advances in computational power. In this paper

we use much deeper and larger CNNs of 12 hidden layers and several billion connections to repre-

sent and learn Go knowledge. We find that this increase in depth and size leads to a qualitative jump

in performance, suggesting that contrary to previous beliefs, a strong move evaluation function for

Go can indeed be represented and learnt by such architectures.

We focus on a supervised learning setup, in which the network is trained to predict expert human

moves, using a large database of professional 19 × 19 Go games. The predictive accuracy of the

Under review as a conference paper at ICLR 2015

CNN on a held-out set of positions reaches 55%, which a significant improvement over the 35%

and 39% predictive accuracy reported for some of the strongest Go programs, and comparable to

the performance of the 6 dan author on the same data set. Furthermore, when the CNN was used

to play games by directly selecting the move recommended by the network output, without any

search, it equalled the performance of state-of-the-art Monte-Carlo search programs (such as Pachi)

that are given 10,000 rollouts per move (i.e., programs that combine handcrafted or shallow prior

knowledge with a search that simulates two million positions), and the first strong Monte-Carlo

search program MoGo with 100,000 rollouts per move. In addition, direct move selection using the

CNN beat GnuGo (a traditional search program) in 97% of games.1

Finally, we demonstrate that the Go knowledge embodied by the CNN can be effectively combined

with Monte-Carlo tree search, by using a delayed prior knowledge procedure. In this approach,

the CNN is evaluated asynchronously on a GPU, and results are incorporated into the main search

procedure once available. Using 100,000 rollouts per move, the overall search defeats the raw CNN

in 87% of games.

2 PRIOR WORK

Convolutional neural networks have a long history in the game of Go. Schraudolph Schraudolph

et al. (1994) trained a simple CNN (exploiting rotational, reflectional, and colour inversion sym-

metries) to predict final territory, by reinforcement learning from games of self-play. The resulting

program beat a simplistic handcrafted program called Wally. NeuroGo (Enzenberger, 1996) used

a more sophisticated architecture to predict final territory, eyes, and connectivity, again exploiting

symmetries; and used a connectivity pathfinder to propagate information across weakly connected

groups of stones. Enzenberger’s program also used reinforcement learning from self-play. When

combined with an alpha-beta search, NeuroGo equalled the performance of GnuGo on 9 × 9 Go,

and reached around 13 kyu on 19 × 19 Go. Sutskever & Nair (2008) applied convolutional net-

works to supervised learning of expert moves, but using a small 1 hidden layer CNN; this matched

the state-of-the-art prediction performance, achieving 34.6% accuracy, but this was not sufficient to

play Go at any reasonable level.

The most successful current programs in Go are based on Monte-Carlo tree search (Kocsis &

Szepesv´ari, 2006). The basic algorithm was augmented in MoGo to use prior knowledge to bootstrap

value estimates in the search tree (Gelly & Silver, 2007); and to use abstractions over subtrees to

accelerate the search (Gelly & Silver, 2011). The strongest current programs such as CrazyStone ap-

ply supervised learning to construct a move selection policy; this is then used to bias the exploration

during search; a faster policy is also learned that selects moves during rollouts (Coulom, 2007).

CrazyStone achieved a 35% move prediction accuracy by extracting a large database of common

patterns from expert games, and combining them into a large linear softmax.

Recent work in image recognition has demonstrated considerable advantages of deep convolutional

networks over alternative architectures. Krizhevsky et al. (2012) were the first to achieve a very large

performance gain with large and deep convolutional neural networks over traditional computer vi-

sion systems. Improved convolutional neural network architectures (primarily in the form of deeper

networks) (Simonyan & Zisserman, 2014) provided another substantial improvement, culminating

with Szegedy et al. (2014), who reduced the error rate of Krizhevsky et al. (2012) from 15.3% top-5

error to 7.0%. The power and generality of large and deep convolutional neural networks suggests

that they may do well on other “visual” domains, such as computer Go.

3 DATA

The dataset used in this work comes from the KGS Go Server. It consists of sequences of board

positions st for complete games played between humans of varying rank. Board state information

includes the position of all stones on the 19x19 board and the sequence allows one to determine the

sequence of moves; a move at is encoded as a 1 of 361 indicator for each position on the 19x19

1 Since we performed this research, we have learned that Clark & Storkey (2014) independently adopted a

similar approach using a smaller 8-layer CNN to achieve 44% move prediction accuracy; and defeated GnuGo

in 86% of games.

Under review as a conference paper at ICLR 2015

Feature

Black / white / empty

Liberties

Liberties after move

Legality

Turns since

Capture size

Ladder move

KGS rank

Planes Description

Stone colour

4 Number of liberties (empty adjacent points)

6 Number of liberties after this move is played

1 Whether point is legal for current player

5 How many turns since a move was played

7 How many opponent stones would be captured

1 Whether a move at this point is a successful ladder capture

9 Rank of current player

Table 1: Features used as inputs to the CNN.

board. We collected 29.4 million board-state next-move pairs (st, at) corresponding to 160,000

games.

Each position st was preprocessed into a set of 19 × 19 feature planes φ(st), that serve as input

to the neural network. The features that we use come directly from the raw representation of the

game rules (stones, liberties, captures, legality, turns since). In addition, we have one simple tactical

feature representing a basic common pattern in Go known as ladders; in practice this adds a small

performance benefit, but the results that we report would be qualitatively similar even without these

features. Many of the features are split into multiple planes of binary values, for example in the case

of liberties there are separate binary features representing whether each intersection has 1 liberty, 2

liberties, 3 liberties, >= 4 liberties. The feature planes are listed in Table 1.2

Finally, we used the following minor innovation. Our dataset consists of games from players of

different strengths. Specifically, the KGS data contains more games by lower dan players, and fewer

games by higher dan players. As a result, a naive approach to training on the KGS data will result in

a network that primarily imitates weaker players. Alternatively, training only on games by stronger

players would result in a massive reduction of training data.

To mitigate this, we provided the network with an additional global inputs indicating the player’s

rank. Specifically we add 9 feature planes each indicating a specific rank. This is like a 1 of 9

encoding that represents the strength of the current player. That is, if the network is learning to

predict a move made by a d dan player, the dth rank feature plane is filled with 1s and the remaining

8 planes are filled with 0s. This has the effect of providing a dynamic bias to the network that

depends on rank.

Because every Go game is symmetric under reflections and rotations, we augmented the dataset by

sampling uniformly from one of the 8 symmetric boards as we filled minibatches in gradient descent.

The dataset was split into a training set of 27.4 million board-state next-move pairs and a test set of

2 million. This split was done before shuffling, so this corresponds to a test set with distinct games.

4 ARCHITECTURE & TRAINING

In this section we describe the precise network architecture and the details of the training procedure.

We used a deep convolutional neural network with 12 weight matrices for each of 12 layers and

rectified linear non-linearities. The first hidden layer’s filters were of size 5×5 and the remainder

were of size 3×3, with a stride of 1. Every layer operated on a 19× 19 input space, with no pooling;

outputs were zero-padded back up up to 19 × 19. The number of filters in each layer ranged from

64 to 192. In addition to convolutions, we also used position-dependent biases (following Sutskever

& Nair (2008)). Our best model has 2.3 million parameters, 630 million connections, and 550,000

hidden units.

The output layer of the CNN was also convolutional with position dependent biases, but with only

two filters. Each produced a 19 × 19 plane, corresponding to inputs to two softmax distributions of

size 361. The first softmax is the distribution over the next move if it is the black player’s turn, and

the second softmax is the distribution over the next move if it is the white player’s move. Although

2Due to the computational cost of running extensive experiments, it is possible that some of these features

are unnecessary or redundant.

Under review as a conference paper at ICLR 2015

both players may often prefer the same move, in general the optimal policy may select different

moves for each player.

We also experimented with weight symmetries Schraudolph et al. (1994). Given that the board is

symmetric, it makes sense to force the filters and biases to be rotationally and reflectionally symmet-

ric, by aggregating weight updates over the 8-fold symmetry group between connections. This type

of symmetry is stronger than the symmetric data augmentation described above, since it enforces

local symmetry of all filters at all locations on the board, not just global symmetry of the entire

board.

For training the network, we used asynchronous stochastic gradient descent (Dean et al., 2012) with

50 replicas each on its own GPU. All parameters were initialized randomly from a uniform[-0.05,

0.05]. Each replica was trained for 25 epochs with a batchsize of 128, a fixed learning rate of 0.128

normalized by batchsize, and no momentum. The network was then fine-tuned on a single GPU

with vanilla SGD for 3 epochs with an annealed learning rate, beginning at half the learning rate for

the asynchronous setting and halved again every epoch. After augmenting the dataset with random

symmetries overfitting was very minor — our 10 layer network overfit by under 1% achieving 55%

on the training set and 54.5% on the test set. Even at the end of training errors on the test set did

not increase. This suggests that we are currently operating in an underfitting regime suggesting that

further improvement is possible. All reported accuracies are on a held out test set.

5 RESULTS

5.1

INVESTIGATION OF WEIGHT SYMMETRIES

We evaluated the effect of weight symmetries on a smaller CNN with 3 and 6 layers respectively.

These networks were trained on a reduced feature set, excluding rank, liberties after move, capture

size, ladder move, and only including a history of one move. The results are given in the table below:

model

3 layer, 64 filters

3 layer, 64 filters, symmetric

6 layer, 192 filters

6 layer, 192 filters, symmetric

% Accuracy

43.3

44.3

49.6

49.4

These results suggest that, perhaps surprisingly, weight symmetries have a strong effect on move

prediction for small and shallow networks, but the effect appeared to disappear completely in larger

and deeper networks.

5.2 ACCURACY AND PLAYING STRENGTH

To understand how the performance depends on network depth, we trained several networks of

different depths. Each CNN used the same architecture as described above, except that the number

of 3 × 3 layers was restricted to 3, 6, 10 and 12 respectively. We measured the prediction accuracy

on the test set, and also the playing strength of the CNN when it was used to directly select moves.

This was achieved by inputting the current position into the network, and selecting the action with

maximum probability in the softmax output for the current player.

Performance was evaluated against the benchmark program GnuGo 3.8, running at its highest level

10. Comparisons are given with reported values for the 3 dan Monte-Carlo search program Aya3;

simultaneously published results on a somewhat shallower CNN Clark & Storkey (2014)4; and also

with the prediction accuracy of a 6 dan human (the second author) on randomly sampled positions

from the test set. All games were scored using Chinese rules, refereed by GnuGo; duplicate games

were excluded from results.

3http://computer-go.org/pipermail/computer-go/2014-December/007018.html

4It should be noted that Clark & Storkey (2014) did not use the highly-predictive turn since feature, because

they believed that it would hurt the network’s play. This is an interesting hypothesis, which this work does not

address.

Under review as a conference paper at ICLR 2015

Figure 1: Probability that the expert’s move is within the top-n predictions of the network. The 10 layer CNN

was omitted for clarity, but it’s performance is only slightly worse than 12 layer. Note y-axis begins at 0.30.

It is apparent from the results that larger and deeper networks have qualitatively better performance

than shallow networks, reaching 97% winning rate against GnuGo for a large 12-layer network

compared to 3.4% for a small 3-layer network. Furthermore, the accuracy on the supervised learning

task is clearly strongly correlated with playing performance, demonstrating that the knowledge learnt

by the network generalises effectively to the real task of evaluating moves.

Size % Accuracy % Wins vs. GnuGo

Depth

16 filters

3 layer

128 filters

3 layer

128 filters

6 layer

10 layer

128 filters

128 filters

12 layer

8 layer (Clark & Storkey, 2014) ≤ 64 filters

Aya 2014

Human 6 dan

37.5

48.0

51.2

54.5

55.2

44.44

38.8

52 ±5.8

stderr

3.4 ± 1.1

61.8 ± 2.6

84.4 ± 1.9

94.7 ± 1.2

97.2 ± 0.9

86 ± 2.5

6 ± 1.0

It is also valuable to know that the correct move is within the network’s n most confident predictions.

If n can be kept small, then this knowledge can be used to reduce the program’s effective search

space. We find that the top-n performance of our network is quite strong; in particular, the network

is able to predict the correct expert move 94% of the time when n = 10.

Next, we compared how the CNN performed when asked to imitate players of different strengths.

We used the same CNN, trained on KGS data of all ranks, and asked it to select moves as if it was

playing according to a specified rank. The opponent was a fixed 10 layer, 128 filter CNN trained

without the KGS rank feature. The results clearly show that the network plays significantly better

when it is asked to imitate a stronger player.

KGS rank % wins vs. 10-layer CNN stderr

49.2 ± 3.6

1 dan

60.1 ± 1.6

5 dan

67.9 ± 5.0

9 dan

Finally, we evaluated the overall strength of the 12-layer CNN when used for move selection, by

playing against several publicly available benchmark programs. All programs were played at the

strongest available settings, and a fixed number of rollouts per move, as specified in the table.

Under review as a conference paper at ICLR 2015

Opponent Rollouts per move Games won by CNN stderr

97.2 ± 0.9

GnuGo

45.9 ± 4.5

MoGo

11.0 ± 2.1

Pachi

12.5 ± 5.8

Fuego

47.4 ± 3.7

Pachi

23.3 ± 7.8

Fuego

100,000

100,000

100,000

10,000

10,000

The neural network is considerably stronger than the traditional search-based program GnuGo, and

its performance is on a par with MoGo with 100,000 rollouts per move (Gelly & Silver, 2007), and

Pachi (a 4 dan MCTS program) running a somewhat reduced search of 10,000 rollouts per move (a

search that visits approximately 2 million positions). It wins more than 10% of games against Fuego

1.1 (Enzenberger et al., 2010) and Pachi 10.99 playing at a strong level (using 100,000 rollouts per

move over 16 threads).5

6 SEARCH

The overreaching goal of this work is to build a strong Go playing program. To this end, we at-

tempted to integrate our move prediction network with Monte Carlo Tree Search (MCTS).

Combining MCTS with a large deep neural network is far from trivial, since the CNN is slower than

the natural speed of the search, and it is not feasible to evaluate every node with the neural network.

The 12-layer network takes 0.15s to evaluate a minibatch of size 128.6

We address this problem by using asynchronous node evaluation. In asynchronous node evaluation,

MCTS builds its search tree and tracks the new nodes that are added into the search tree. When the

number of new nodes equals the minibatch size, all these new positions are submitted to the CNN for

evaluation on a GPU. The GPU computes the move recommendations, while the search continues in

parallel. Once the GPU computation is complete, the prior knowledge in the new nodes is updated to

contain move evaluations from the CNN. The network evaluates the nodes in a FIFO order, in order

to maximally influence the search tree. By using a single machine with 16 Intel® Xeon® CPU E5-

2643 v2 @ 3.50GHz and and 4 GeForce GTX Titan Black GPUs, we are able to maintain a MCTS

search at approximately 47,000 rollouts per second, without dropping CNN evaluations. However,

it should be noted that the performance of asynchronous node evaluation is significantly less than a

fully synchronous and serial implementation, since new information from the search is only utilised

after a significant lag (around 0.15s in our case), due to the GPU computation.

In addition, the MCTS engine utilised standard heuristics for computer Go: RAVE (Gelly & Silver,

2011), a UCT exploration strategy similar to Chaslot et al. (2008), and very simple rollouts based

solely on 3 × 3 patterns (Huang et al., 2011).

We measured the performance of the search-based program by playing games between the 12-layer

CNN with MCTS, and a baseline 12-layer CNN without any search. Using 100,000 rollouts per

move, the search-based program beats the baseline CNN in 87% of games.

Rollouts per move % wins against baseline

100,000

10,000

stderr

86.7 ± 3.5

67.6 ± 2.6

7 DISCUSSION

In this work, we showed that large deep convolutional neural networks can predict the next move

made by Go experts with an accuracy that exceeds previous methods by a large margin, approxi-

mately matching human performance. Furthermore, this predictive accuracy translates into much

stronger move evaluation and playing strength than has previously been possible. Without any

5The 8-layer network of Clark & Storkey (2014) won 12% of games against Fuego using time limits corre-

sponding to approximately 5,000 rollouts per move.

6Reducing the minibatch size does not significantly speed up end-to-end computation time in our GPU

implementation.

Under review as a conference paper at ICLR 2015

Figure 2: A game played between the 12-layer CNN (without any search) and Fuego (using 100k roll-

outs/move). The CNN plays white.

search, the network is able to outperform traditional search based programs such as GnuGo, and

compete with state-of-the-art MCTS programs such as Pachi and Fuego.

In Figure 2 we present a sample game played by the 12-layer CNN (with no search) versus Fuego

(searching 100K rollouts per move) which was won by the neural network player. It is clear that

the neural network has implicitly understood many sophisticated aspects of Go, including good

shape (patterns that maximise long term effectiveness of stones), Fuseki (opening sequences), Joseki

(corner patterns), Tesuji (tactical patterns), Ko fights (intricate tactical battles involving repeated

recapture of the same stones), territory (ownership of points), and influence (long-term potential

for territory). It is remarkable that a single, unified, straightforward architecture can master these

elements of the game to such a degree, and without any explicit lookahead.

On the other hand, we note that the network still has weaknesses: notably it sometimes fails to under-

stand the global picture, behaving as if the life and death status of large groups has been incorrectly

assessed. Interestingly, it is precisely these global aspects of the game for which Monte-Carlo search

excels, suggesting that these two techniques may be largely complementary. We have provided a

preliminary proof-of-concept that MCTS and deep neural networks may be combined effectively. It

appears that we now have two core elements that scale effectively with increased computational re-

source: scalable planning, using Monte-Carlo search; and scalable evaluation functions, using deep

neural networks. In the future, as parallel computation units such as GPUs continue to increase in

performance, we believe that this trajectory of research will lead to considerably stronger programs

than are currently possible.

Under review as a conference paper at ICLR 2015

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