1 code implementation • 25 Jan 2024 • Weihao Tan, Wentao Zhang, Shanqi Liu, Longtao Zheng, Xinrun Wang, Bo An
Despite the impressive performance across numerous tasks, large language models (LLMs) often fail in solving simple decision-making tasks due to the misalignment of the knowledge in LLMs with environments.
1 code implementation • 14 Feb 2023 • Shanqi Liu, Yujing Hu, Runze Wu, Dong Xing, Yu Xiong, Changjie Fan, Kun Kuang, Yong liu
We first illustrate that the proposed value decomposition can consider the complicated interactions among agents and is feasible to learn in large-scale scenarios.
no code implementations • 8 Feb 2021 • Guangming Yao, Yi Yuan, Tianjia Shao, Shuang Li, Shanqi Liu, Yong liu, Mengmeng Wang, Kun Zhou
The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance.
no code implementations • 5 Nov 2020 • Shanqi Liu, Junjie Cao, Wenzhou Chen, Licheng Wen, Yong liu
In this work, we propose a new imitation learning approach called Hierarchical Imitation Learning from Observation(HILONet), which adopts a hierarchical structure to choose feasible sub-goals from demonstrated observations dynamically.
no code implementations • 4 Nov 2020 • Shanqi Liu, Licheng Wen, Jinhao Cui, Xuemeng Yang, Junjie Cao, Yong liu
We also deploy and validate our method in a real world scenario.
Robotics Multiagent Systems