Neural Episodic Control
This addresses the problem of slow learning in deep reinforcement learning for AI researchers, offering a novel approach to improve efficiency.
The paper tackles the inefficiency of deep reinforcement learning agents by proposing Neural Episodic Control, which uses a semi-tabular representation to rapidly assimilate experiences, resulting in significantly faster learning across various environments.
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.