Contrastive Successor Features recover ground-truth RL states up to a linear map whenever the skill-conditioned transition differences follow a von Mises-Fisher distribution and policies are diverse.
Deep Data Density Estimation through Donsker-Varadhan Representation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Estimating the data density is one of the challenging problems in deep learning. In this paper, we present a simple yet effective method for estimating the data density using a deep neural network and the Donsker-Varadhan variational lower bound on the KL divergence. We show that the optimal critic function associated with the Donsker-Varadhan representation on the KL divergence between the data and the uniform distribution can estimate the data density. We also present the deep neural network-based modeling and its stochastic learning. The experimental results and possible applications of the proposed method demonstrate that it is competitive with the previous methods and has a lot of possibilities in applied to various applications.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Contrastive Successor Features recover ground-truth RL states up to a linear map whenever the skill-conditioned transition differences follow a von Mises-Fisher distribution and policies are diverse.