Decision-Centered Abstractions via Orthogonal Estimation of Difference-of-Q Functions
This work introduces state abstractions that target preservation of the difference-of-Q functions, and proposes to learn these abstractions via causal machine learning of the difference-of-Q function and standard statistical sparse learning, under a nonparametric additive-rewards model.
Defu Cao, Angela Zhou
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