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.
['Andrei A. Rusu', 'Neil C. Rabinowitz', 'Guillaume Desjardins', 'Hubert Soyer', 'James Kirkpatrick', 'Koray Kavukcuoglu', 'Razvan Pascanu', 'Raia Hadsell']
Progressive Neural Networks