  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.
