  Traditionally, learning from human demonstrations via direct behavior cloning
can lead to high-performance policies given that the algorithm has access to
large amounts of high-quality data covering the most likely scenarios to be
encountered when the agent is operating. However, in real-world scenarios,
expert data is limited and it is desired to train an agent that learns a
behavior policy general enough to handle situations that were not demonstrated
by the human expert. Another alternative is to learn these policies with no
supervision via deep reinforcement learning, however, these algorithms require
a large amount of computing time to perform well on complex tasks with
high-dimensional state and action spaces, such as those found in StarCraft II.
Automatic curriculum learning is a recent mechanism comprised of techniques
designed to speed up deep reinforcement learning by adjusting the difficulty of
the current task to be solved according to the agent's current capabilities.
Designing a proper curriculum, however, can be challenging for sufficiently
complex tasks, and thus we leverage human demonstrations as a way to guide
agent exploration during training. In this work, we aim to train deep
reinforcement learning agents that can command multiple heterogeneous actors
where starting positions and overall difficulty of the task are controlled by
an automatically-generated curriculum from a single human demonstration. Our
results show that an agent trained via automated curriculum learning can
outperform state-of-the-art deep reinforcement learning baselines and match the
performance of the human expert in a simulated command and control task in
StarCraft II modeled over a real military scenario.
