Policy Evaluation Using the Ω-Return

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris


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.