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arXiv · 1910.07478

Adaptive Trade-Offs in Off-Policy Learning

Mark Rowland·Will Dabney·Rémi Munos
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Abstract

A great variety of off-policy learning algorithms exist in the literature, and new breakthroughs in this area continue to be made, improving theoretical understanding and yielding state-of-the-art reinforcement learning algorithms. In this paper, we take a unifying view of this space of algorithms, and consider their trade-offs of three fundamental quantities: update variance, fixed-point bias, and contraction rate. This leads to new perspectives of existing methods, and also naturally yields novel algorithms for off-policy evaluation and control. We develop one such algorithm, C-trace, demonstrating that it is able to more efficiently make these trade-offs than existing methods in use, and that it can be scaled to yield state-of-the-art performance in large-scale environments.

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Mark Rowland, Will Dabney, Rémi Munos. 2020-07-30. Adaptive Trade-Offs in Off-Policy Learning. https://arxiv.org/abs/1910.07478

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