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We introduce the eigentask framework for lifelong learning. An eigentask is apairing of a skill that solves a set of related tasks, paired with a generativemodel that can sample from the skill's input space. The framework extendsgenerative replay approaches, which have mainly been used to avoid catastrophicforgetting, to also address other lifelong learning goals such as forwardknowledge transfer. We propose a wake-sleep cycle of alternating task learningand knowledge consolidation for learning in our framework, and instantiate itfor lifelong supervised learning and lifelong RL. We achieve improvedperformance over the state-of-the-art in supervised continual learning, andshow evidence of forward knowledge transfer in a lifelong RL application in thegame Starcraft2.

Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer