Methods › Reinforcement Learning › Density Ratio Learning › GradientDICE
GradientDICE
Introduced by Shangtong Zhang et al. in GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
GradientDICE is a density ratio learning method for estimating the density ratio between the state distribution of the target policy and the sampling distribution in off-policy reinforcement learning. It optimizes a different objective from GenDICE by using the Perron-Frobenius theorem and eliminating GenDICE’s use of divergence, such that nonlinearity in parameterization is not necessary for GradientDICE, which is provably convergent under linear function approximation.
Papers archive 2025-07-28
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GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values 29 Jan 2020 · 1 repository · arXiv:2001.11113
Tasks archive 2025-07-28
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| Task | Papers |
|---|---|
| Reinforcement Learning | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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