  In this work, we present a MCTS-based Go-playing program which uses
convolutional networks in all parts. Our method performs MCTS in batches,
explores the Monte Carlo search tree using Thompson sampling and a
convolutional network, and evaluates convnet-based rollouts on the GPU. We
achieve strong win rates against open source Go programs and attain competitive
results against state of the art convolutional net-based Go-playing programs.
