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Policy Evaluation Using the Ω-Return

Conference Paper Artificial Intelligence · Machine Learning

Abstract

We propose the Ω-return as an alternative to the λ-return currently used by the TD(λ) family of algorithms. The benefit of the Ω-return is that it accounts for the correlation of different length returns. Because it is difficult to compute exactly, we suggest one way of approximating the Ω-return. We provide empirical studies that suggest that it is superior to the λ-return and γ-return for a variety of problems.

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
1111637546766369065
v2026.09.13