Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II. (arXiv:2205.05784v1 [cs.LG])

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 …

arxiv curriculum curriculum learning human ii learning