Reinforcement learning, fast and slow

Abstract

Recent AI research has given rise to powerful techniques for deep reinforcement learning. In their combination of representation learning with reward-driven behavior, deep reinforcement learning would appear to have inherent interest for psychology and neuroscience.
One reservation has been that deep reinforcement learning procedures demand large amounts of training data, suggesting that these algorithms may differ fundamentally from those underlying human learning.
While this concern applies to the initial wave of deep RL techniques, subsequent AI work has established methods that allow deep RL systems to learn more quickly and efficiently. Two particularly interesting and promising techniques center, respectively, on episodic memory and meta-learning.
Alongside their interest as AI techniques, deep RL methods leveraging episodic memory and meta-learning have direct and interesting implications for psychology and neuroscience. One subtle but critically important insight which these techniques bring into focus is the fundamental connection between fast and slow forms of learning.

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