no code implementations • 8 Feb 2024 • Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob Foerster
Providing theoretical guarantees for M-FOS is hard because A) there is little literature on theoretical sample complexity bounds for meta-reinforcement learning B) M-FOS operates in continuous state and action spaces, so theoretical analysis is challenging.
no code implementations • 17 Jan 2024 • Yao Lu, Song Bian, Lequn Chen, Yongjun He, Yulong Hui, Matthew Lentz, Beibin Li, Fei Liu, Jialin Li, Qi Liu, Rui Liu, Xiaoxuan Liu, Lin Ma, Kexin Rong, Jianguo Wang, Yingjun Wu, Yongji Wu, Huanchen Zhang, Minjia Zhang, Qizhen Zhang, Tianyi Zhou, Danyang Zhuo
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
1 code implementation • 14 Jul 2021 • Qizhen Zhang, Chris Lu, Animesh Garg, Jakob Foerster
We also learn a centralized exploration policy within our model that learns to collect additional data in state-action regions with high model uncertainty.