  Learning to solve complex sequences of tasks--while both leveraging transfer
and avoiding catastrophic forgetting--remains a key obstacle to achieving
human-level intelligence. The progressive networks approach represents a step
forward in this direction: they are immune to forgetting and can leverage prior
knowledge via lateral connections to previously learned features. We evaluate
this architecture extensively on a wide variety of reinforcement learning tasks
(Atari and 3D maze games), and show that it outperforms common baselines based
on pretraining and finetuning. Using a novel sensitivity measure, we
demonstrate that transfer occurs at both low-level sensory and high-level
control layers of the learned policy.
