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.
Progressive Neural Networks REF learn to solve complex sequences of task by leveraging prior knowledge with lateral connections.
Progressive Neural Networks
ArXiv
ArXiv
Computer Science