Year: 2017
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Pritzel, Alexander, Uria, Benigno, Srinivasan, Sriram, Puigdomènech, Adrià, Vinyals, Oriol, Hassabis, Demis, Wierstra, Daan, Blundell, Charles
Institution: Google DeepMind (United Kingdom)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: DeepMind
OpenAlex ID: W2594466397