“Neural Episodic Control”, Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell2017-03-06 (dynamic evaluation (NN), retrieval AI, model-free RL; similar)⁠:

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 faster than other state-of-the-art, general purpose deep reinforcement learning agents.

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