Neural Episodic Control
Authors: Alex Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell
Deep reinforcement learning algorithms have achieved state of the art performance on a variety of tasks, however they tend to be grossly data inefficient. In this work we propose a novel algorithm that allows rapid incorporation of new information collected by the agent. For this we introduce a new differentiable data structure, a differentiable neural dictionary, that can incorporate new information immediately, while being able to update it’s internal representation based on the task the algorithm is supposed to solve. Our agent, Neural Episodic Control, is built on top of the differentiable data structure and is able to learn significantly faster across a wide range of environments.
For further details and related work, please see the paper.
Check it out at ICML:
Wednesday 09 August, 16:06-16:24 @ C4.5
Wednesday 09 August, 18:30-22:00 @ Gallery #125
Source: https://deepmind.com/blog/article/icml-round-papers-part-three
