  We propose a lifelong learning system that has the ability to reuse and
transfer knowledge from one task to another while efficiently retaining the
previously learned knowledge-base. Knowledge is transferred by learning
reusable skills to solve tasks in Minecraft, a popular video game which is an
unsolved and high-dimensional lifelong learning problem. These reusable skills,
which we refer to as Deep Skill Networks, are then incorporated into our novel
Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using
two techniques: (1) a deep skill array and (2) skill distillation, our novel
variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill
distillation enables the HDRLN to efficiently retain knowledge and therefore
scale in lifelong learning, by accumulating knowledge and encapsulating
multiple reusable skills into a single distilled network. The H-DRLN exhibits
superior performance and lower learning sample complexity compared to the
regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft.
