  We introduce the eigentask framework for lifelong learning. An eigentask is a
pairing of a skill that solves a set of related tasks, paired with a generative
model that can sample from the skill's input space. The framework extends
generative replay approaches, which have mainly been used to avoid catastrophic
forgetting, to also address other lifelong learning goals such as forward
knowledge transfer. We propose a wake-sleep cycle of alternating task learning
and knowledge consolidation for learning in our framework, and instantiate it
for lifelong supervised learning and lifelong RL. We achieve improved
performance over the state-of-the-art in supervised continual learning, and
show evidence of forward knowledge transfer in a lifelong RL application in the
game Starcraft2.
