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The Ethereal
Anderson Acceleration for Reinforcement Learning
September 25, 2018 ยท Declared Dead ยท ๐ European Workshop on Reinforcement Learning
"No code URL or promise found in abstract"
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Authors
Matthieu Geist, Bruno Scherrer
arXiv ID
1809.09501
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
42
Venue
European Workshop on Reinforcement Learning
Last Checked
6 months ago
Abstract
Anderson acceleration is an old and simple method for accelerating the computation of a fixed point. However, as far as we know and quite surprisingly, it has never been applied to dynamic programming or reinforcement learning. In this paper, we explain briefly what Anderson acceleration is and how it can be applied to value iteration, this being supported by preliminary experiments showing a significant speed up of convergence, that we critically discuss. We also discuss how this idea could be applied more generally to (deep) reinforcement learning.
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