Applying supervised and reinforcement learning methods to create
  neural-network-based agents for playing StarCraft II
  Recently, multiple approaches for creating agents for playing various complex
real-time computer games such as StarCraft II or Dota 2 were proposed, however,
they either embed a significant amount of expert knowledge into the agent or
use a prohibitively large for most researchers amount of computational
resources. We propose a neural network architecture for playing the full
two-player match of StarCraft II trained with general-purpose supervised and
reinforcement learning, that can be trained on a single consumer-grade PC with
a single GPU. We also show that our implementation achieves a non-trivial
performance when compared to the in-game scripted bots. We make no simplifying
assumptions about the game except for playing on a single chosen map, and we
use very little expert knowledge. In principle, our approach can be applied to
any RTS game with small modifications. While our results are far behind the
state-of-the-art large-scale approaches in terms of the final performance, we
believe our work can serve as a solid baseline for other small-scale
experiments.
