Improving Model and Search for Computer Go
  The standard for Deep Reinforcement Learning in games, following Alpha Zero,
is to use residual networks and to increase the depth of the network to get
better results. We propose to improve mobile networks as an alternative to
residual networks and experimentally show the playing strength of the networks
according to both their width and their depth. We also propose a generalization
of the PUCT search algorithm that improves on PUCT.
