  Humans often think of complex tasks as combinations of simpler subtasks in
order to learn those complex tasks more efficiently. For example, a backflip
could be considered a combination of four subskills: jumping, tucking knees,
rolling backwards, and thrusting arms downwards. Motivated by this line of
reasoning, we propose a new algorithm that trains neural network policies on
simple, easy-to-learn skills in order to cultivate latent spaces that
accelerate imitation learning of complex, hard-to-learn skills. We focus on the
case in which the complex task comprises a concurrent (and possibly sequential)
combination of the simpler subtasks, and therefore our algorithm can be seen as
a novel approach to concurrent hierarchical imitation learning. We evaluate our
algorithm on difficult tasks in a high-dimensional environment and find that it
consistently outperforms a state-of-the-art baseline in training speed and
overall performance.
