  The real-time strategy game of StarCraft II has been posed as a challenge for
reinforcement learning by Google's DeepMind. This study examines the use of an
agent based on the Monte-Carlo Tree Search algorithm for optimizing the build
order in StarCraft II, and discusses how its performance can be improved even
further by combining it with a deep reinforcement learning neural network. The
experimental results accomplished using Monte-Carlo Tree Search achieves a
score similar to a novice human player by only using very limited time and
computational resources, which paves the way to achieving scores comparable to
those of a human expert by combining it with the use of deep reinforcement
learning.
