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arXiv · 1910.07479

Conditional Importance Sampling for Off-Policy Learning

Mark Rowland·Anna Harutyunyan·Hado van Hasselt·Diana Borsa·Tom Schaul·Rémi Munos·Will Dabney
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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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BibTeXRIS

Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney. 2020-07-30. Conditional Importance Sampling for Off-Policy Learning. https://arxiv.org/abs/1910.07479

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