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arXiv · 1507.01569

Emphatic Temporal-Difference Learning

A. Rupam Mahmood·Huizhen Yu·Martha White·Richard S. Sutton
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Abstract

Emphatic algorithms are temporal-difference learning algorithms that change their effective state distribution by selectively emphasizing and de-emphasizing their updates on different time steps. Recent works by Sutton, Mahmood and White (2015), and Yu (2015) show that by varying the emphasis in a particular way, these algorithms become stable and convergent under off-policy training with linear function approximation. This paper serves as a unified summary of the available results from both works. In addition, we demonstrate the empirical benefits from the flexibility of emphatic algorithms, including state-dependent discounting, state-dependent bootstrapping, and the user-specified allocation of function approximation resources.

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A. Rupam Mahmood, Huizhen Yu, Martha White, Richard S. Sutton. 2015-07-06. Emphatic Temporal-Difference Learning. https://arxiv.org/abs/1507.01569

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