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arXiv · 1810.08163

Fast deep reinforcement learning using online adjustments from the past

Steven Hansen·Pablo Sprechmann·Alexander Pritzel·André Barreto·Charles Blundell
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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 planning 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 EVAis performant on a demonstration task and Atari games.

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Steven Hansen, Pablo Sprechmann, Alexander Pritzel, André Barreto, Charles Blundell. 2018-10-18. Fast deep reinforcement learning using online adjustments from the past. https://arxiv.org/abs/1810.08163

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