First steps in reinforcement learning towards continual learning and transfering knowledge to new domains.
Still restricted to compatible source and task domains.
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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