  We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that
(1) learns skills (i.e., temporally extended actions or options) as well as (2)
where to apply them. We believe that both (1) and (2) are necessary for a truly
general skill learning framework, which is a key building block needed to scale
up to lifelong learning agents. The ASAP framework can also solve related new
tasks simply by adapting where it applies its existing learned skills. We prove
that ASAP converges to a local optimum under natural conditions. Finally, our
experimental results, which include a RoboCup domain, demonstrate the ability
of ASAP to learn where to reuse skills as well as solve multiple tasks with
considerably less experience than solving each task from scratch.
