Search Results for author:
Found 6 papers, 2 papers with code
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
A learning policy may take actions beyond the behavior policy's knowledge, referred to as Out-of-Distribution (OOD) actions.
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning
In this paper, we propose Diffusion Actor-Critic (DAC) that formulates the Kullback-Leibler (KL) constraint policy iteration as a diffusion noise regression problem, enabling direct representation of target policies as diffusion models.
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement Learning
Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points.
Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition
We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases.
Regularized maximum correntropy machine
To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework.
