Progressive Neural Networks

Andrei A. Rusu; Neil C. Rabinowitz; Guillaume Desjardins; Hubert Soyer; James Kirkpatrick; Koray Kavukcuoglu; Razvan Pascanu; Raia Hadsell

arXiv · 1606.04671

Progressive Neural Networks

Andrei A. Rusu·Neil C. Rabinowitz·Guillaume Desjardins·Hubert Soyer·James Kirkpatrick·Koray Kavukcuoglu·Razvan Pascanu·Raia Hadsell

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Abstract

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.

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Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, Raia Hadsell. 2016-06-15. Progressive Neural Networks. https://arxiv.org/abs/1606.04671

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