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Poster

Fast deep reinforcement learning using online adjustments from the past

Steven Hansen · Alexander Pritzel · Pablo Sprechmann · Andre Barreto · Charles Blundell

Room 210 #34

Keywords: [ Reinforcement Learning ] [ Memory-Augmented Neural Networks ]


Abstract:

We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer. EVA shifts the value predicted by a neural network with an estimate of the value function found by prioritised sweeping over experience tuples from the replay buffer near the current state. EVA combines a number of recent ideas around combining episodic memory-like structures into reinforcement learning agents: slot-based storage, content-based retrieval, and memory-based planning. We show that EVA is performant on a demonstration task and Atari games.

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