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Poster

Neural Episodic Control

Alexander Pritzel · Benigno Uria · Srinivasan Sriram · AdriĆ  Puigdomenech Badia · Oriol Vinyals · Demis Hassabis · Daan Wierstra · Charles Blundell

Gallery #125

Abstract:

Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them. Our agent uses a semi-tabular representation of the value function: a buffer of past experience containing slowly changing state representations and rapidly updated estimates of the value function. We show across a wide range of environments that our agent learns significantly faster than other state-of-the-art, general purpose deep reinforcement learning agents.

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