ICML Conference 2017 Conference Paper
Neural Episodic Control
- Alexander Pritzel
- Benigno Uria
- Sriram Srinivasan 0005
- Adrià Puigdomènech Badia
- Oriol Vinyals
- Demis Hassabis
- Daan Wierstra
- Charles Blundell
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.