Reinforcement Learning
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
Left: The reward function is a mixture of Gaussian, and the offline data distribution is unbalanced with most samples located in low-reward states.
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning Liyuan Mao Shanghai Jiao Tong University Haoran Xu UT Austin Xianyuan Zhan Tsinghua University Weinan Zhang
Based on this, we propose a novel approach, Diffusion-DICE, that directly performs this transformation using diffusion models.
Extracting Training Data from Molecular Pre-trained Models
This work, for the first time, explores the risks of extracting private training molecular data from molecular pre-trained models. This task is nontrivial as the molecular pre-trained models are non-generative and exhibit a diversity of model architectures, which differs significantly from language and image models.
Diffusion-based Curriculum Reinforcement Learning
V arious approaches have been proposed for the generation of curriculum goals.
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
In such cases, re-training the model from scratch could be time-consuming.
dd03f856fc7f2efeec8b1c796284561d-Paper-Conference.pdf
Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning Qi Wang 1, 2 Junming Y ang 3 Y unbo Wang
Training offline RL models using visual inputs poses two significant challenges, i.e.,
Iterative Reachability Estimation for Safe Reinforcement Learning
We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework.
