J Guan, L Shen, A Zhou, L Li, H Hu… - Proceedings of the …, 2024 - openaccess.thecvf.com
Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy both cumulative and state-wise costs from offline datasets. This arrangement provides an …
Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data …
J Hou, G Chen, Z Li, W He, S Gu… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Industries, such as manufacturing, are accelerating their embrace of the metaverse to achieve higher productivity, especially in complex industrial scheduling. In view of the …
L Huang, B Dong, W Xie… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded datasets without online environment interaction. It is unfortunate that extrapolation errors can …
T Zhang, J Guan, L Zhao, Y Li, D Li, Z Zeng… - arXiv preprint arXiv …, 2024 - arxiv.org
Offline reinforcement learning (RL) aims to learn optimal policies from previously collected datasets. Recently, due to their powerful representational capabilities, diffusion models have …