GradientDICE
GradientDICE
**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](https://arxiv.org/abs/2002.09072) 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.
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
https://paperswithcode.com/paper/gradientdice-rethinking-generalized-offline
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values