Batch Monte Carlo Tree Search
  Making inferences with a deep neural network on a batch of states is much
faster with a GPU than making inferences on one state after another. We build
on this property to propose Monte Carlo Tree Search algorithms using batched
inferences. Instead of using either a search tree or a transposition table we
propose to use both in the same algorithm. The transposition table contains the
results of the inferences while the search tree contains the statistics of
Monte Carlo Tree Search. We also propose to analyze multiple heuristics that
improve the search: the $\mu$ FPU, the Virtual Mean, the Last Iteration and the
Second Move heuristics. They are evaluated for the game of Go using a MobileNet
neural network.
