Modular Architecture for StarCraft II with Deep Reinforcement Learning
  We present a novel modular architecture for StarCraft II AI. The architecture
splits responsibilities between multiple modules that each control one aspect
of the game, such as build-order selection or tactics. A centralized scheduler
reviews macros suggested by all modules and decides their order of execution.
An updater keeps track of environment changes and instantiates macros into
series of executable actions. Modules in this framework can be optimized
independently or jointly via human design, planning, or reinforcement learning.
We apply deep reinforcement learning techniques to training two out of six
modules of a modular agent with self-play, achieving 94% or 87% win rates
against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches,
with or without fog-of-war.
