  We introduce a conceptually simple and scalable framework for continual
learning domains where tasks are learned sequentially. Our method is constant
in the number of parameters and is designed to preserve performance on
previously encountered tasks while accelerating learning progress on subsequent
problems. This is achieved by training a network with two components: A
knowledge base, capable of solving previously encountered problems, which is
connected to an active column that is employed to efficiently learn the current
task. After learning a new task, the active column is distilled into the
knowledge base, taking care to protect any previously acquired skills. This
cycle of active learning (progression) followed by consolidation (compression)
requires no architecture growth, no access to or storing of previous data or
tasks, and no task-specific parameters. We demonstrate the progress & compress
approach on sequential classification of handwritten alphabets as well as two
reinforcement learning domains: Atari games and 3D maze navigation.
