Neural Episodic Control

Alexander Pritzel; Benigno Uria; Sriram Srinivasan; Adrià Puigdomènech; Oriol Vinyals; Demis Hassabis; Daan Wierstra; Charles Blundell

arXiv · 1703.01988

Neural Episodic Control

Alexander Pritzel·Benigno Uria·Sriram Srinivasan·Adrià Puigdomènech·Oriol Vinyals·Demis Hassabis·Daan Wierstra·Charles Blundell

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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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Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell. 2017-03-06. Neural Episodic Control. https://arxiv.org/abs/1703.01988

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