AlphaGo Use Deep Learning to Play Go

Introduction

AlphaGo is a computer program developed by Google's DeepMind to play Go, the ancient Chinese board game. In March 2016, AlphaGo beat Lee Sedol, one of the world's top Go players, in a five-game match, marking a huge milestone in artificial intelligence. AlphaGo's success is attributed to its use of deep learning, a subfield of machine learning that enables computers to learn from experience and make predictions.

Understanding Go

Go is a two-player game played on a 19x19 board. The objective of the game is to encircle and capture the opponent's stones. Go has a much larger branching factor than chess, which means there are many more possible moves to consider in each turn.

Deep Learning

Deep Learning is a subset of machine learning, which in turn is a subset of artificial intelligence. Deep learning involves training artificial neural networks (ANNs) to perform a task. ANNs are modeled after the human brain, with layers of neurons connected by synapses. The more layers the network has, the deeper it is, and the more complex tasks it can perform.

Training the Neural Network

To train AlphaGo, DeepMind collected millions of positions from expert games and generated further positions by playing against itself. They then used a neural network to predict the next move, based on the current position. To improve the accuracy of the predictions, they used a technique called Monte Carlo tree search (MCTS). MCTS involves simulating many possible games from the current position and selecting the move that leads to the highest win rate.

AlphaGo used a novel neural network architecture called a convolutional neural network (CNN), which was specifically designed for Go. This architecture enabled AlphaGo to learn the features of the game board more effectively.

Code Example

Here is an example of how to implement a simple neural network for Go using TensorFlow, a popular deep learning library:

import tensorflow as tf

# Define the input and output placeholders
x = tf.placeholder(tf.float32, shape=(None, 19, 19, 17))
y = tf.placeholder(tf.float32, shape=(None, 19*19))

# Define the convolutional layers
conv1 = tf.layers.conv2d(x, filters=64, kernel_size=3, padding="SAME", activation=tf.nn.relu)
conv2 = tf.layers.conv2d(conv1, filters=128, kernel_size=3, padding="SAME", activation=tf.nn.relu)

# Flatten the output of the convolutional layers
flat = tf.layers.flatten(conv2)

# Define the fully connected layers
fc1 = tf.layers.dense(flat, units=256, activation=tf.nn.relu)
fc2 = tf.layers.dense(fc1, units=19*19, activation=None)

# Define the loss function and optimizer
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc2, labels=y))
train