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.
Most relevant to us are the progressive networks of REF , originally proposed for reinforcement learning.
Progressive Neural Networks
ArXiv
ArXiv
Computer Science