  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.
