Mobile Networks for Computer Go

The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines. For example the use of residual networks gave a 600 ELO increase in the strength of Alpha Go. This paper proposes to evaluate the interest of Mobile Network for the game of Go using supervised learning as well as the use of a policy head and a value head different from the Alpha Zero heads. The accuracy of the policy, the mean squared error of the value, the efficiency of the networks with the number of parameters, the playing speed and strength of the trained networks are evaluated. I gave the students a Python library I programmed in C so as This paper is about the efficiency of neural networks trained to randomly build batches of tensors representing states that to play the game of Go. Mobile Networks [1], [2] are commonly could be used to give inputs and outputs to the networks. I also used in computer vision to classify images.