Fast deep reinforcement learning using online adjustments from the past
Machine Learning
2018-10-19 v1 Artificial Intelligence
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.
Cite
@article{arxiv.1810.08163,
title = {Fast deep reinforcement learning using online adjustments from the past},
author = {Steven Hansen and Pablo Sprechmann and Alexander Pritzel and André Barreto and Charles Blundell},
journal= {arXiv preprint arXiv:1810.08163},
year = {2018}
}
Comments
Accepted at NIPS 2018