Contents
What is input in artificial neural network?
The input layer of a neural network is composed of artificial input neurons, and brings the initial data into the system for further processing by subsequent layers of artificial neurons. The input layer is the very beginning of the workflow for the artificial neural network.
What type of neural network is AlphaGo?
Convolutional Neural Networks
Alpha Go Zero is made of a Convolutional Neural Networks and a Monte Carlo Tree. It is trained in self-play with Reinforcement Learning algorithms.
Is AlphaGo deep learning?
AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play.
How does AlphaGo work with other neural networks?
One neural network, the “policy network”, selects the next move to play. The other neural network, the “value network”, predicts the winner of the game. We introduced AlphaGo to numerous amateur games to help it develop an understanding of reasonable human play.
What kind of computer program is AlphaGo?
We created AlphaGo, a computer program that combines advanced search tree with deep neural networks. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections.
Which is the most important aspect of AlphaGo?
One of the most important aspects of AlphaGo is learning-ability. Deep learning allows AlphaGo to continually improve its intelligence by playing a large number of games against itself. This trains the policy network to help AlphaGo predict the next moves, which in turn trains the value network to ascertain and evaluate those positions [5].
How is the Policy Network used in AlphaGo?
Policy network is used to generate prior probabilities to guide what move the Monte-Carlo search should pick. Value network is used to generate data to validate the policy network. If the policy network is bad, AlphaGo would need more computing resources to converge (if ever).