Learning to Compose Skills
  We present a differentiable framework capable of learning a wide variety of
compositions of simple policies that we call skills. By recursively composing
skills with themselves, we can create hierarchies that display complex
behavior. Skill networks are trained to generate skill-state embeddings that
are provided as inputs to a trainable composition function, which in turn
outputs a policy for the overall task. Our experiments on an environment
consisting of multiple collect and evade tasks show that this architecture is
able to quickly build complex skills from simpler ones. Furthermore, the
learned composition function displays some transfer to unseen combinations of
skills, allowing for zero-shot generalizations.
