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The Ethereal
Conditional Importance Sampling for Off-Policy Learning
October 16, 2019 ยท Declared Dead ยท ๐ International Conference on Artificial Intelligence and Statistics
"No code URL or promise found in abstract"
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Authors
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rรฉmi Munos, Will Dabney
arXiv ID
1910.07479
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
19
Venue
International Conference on Artificial Intelligence and Statistics
Last Checked
5 months ago
Abstract
The principal contribution of this paper is a conceptual framework for off-policy reinforcement learning, based on conditional expectations of importance sampling ratios. This framework yields new perspectives and understanding of existing off-policy algorithms, and reveals a broad space of unexplored algorithms. We theoretically analyse this space, and concretely investigate several algorithms that arise from this framework.
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