no code implementations • 10 Dec 2023 • Kunyang Lin, Yufeng Wang, Peihao Chen, Runhao Zeng, Siyuan Zhou, Mingkui Tan, Chuang Gan
In this paper, we propose a new approach that enables agents to learn whether their behaviors should be consistent with that of other agents by utilizing intrinsic rewards to learn the optimal policy for each agent.
Multi-agent Reinforcement Learning reinforcement-learning +2
1 code implementation • ICCV 2023 • Kunyang Lin, Peihao Chen, Diwei Huang, Thomas H. Li, Mingkui Tan, Chuang Gan
In this paper, we propose to learn an agent from these videos by creating a large-scale dataset which comprises reasonable path-instruction pairs from house tour videos and pre-training the agent on it.
1 code implementation • 25 Oct 2022 • Lizhao Liu, Kunyang Lin, Shangxin Huang, Zhongli Li, Chao Li, Yunbo Cao, Qingyu Zhou
Moreover, there are no standardized benchmarks to provide a fair comparison between different stroke extraction methods, which, we believe, is a major impediment to the development of Chinese character stroke understanding and related tasks.
1 code implementation • 14 Oct 2022 • Peihao Chen, Dongyu Ji, Kunyang Lin, Runhao Zeng, Thomas H. Li, Mingkui Tan, Chuang Gan
To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects.
no code implementations • 14 Oct 2022 • Peihao Chen, Dongyu Ji, Kunyang Lin, Weiwen Hu, Wenbing Huang, Thomas H. Li, Mingkui Tan, Chuang Gan
How to make robots perceive the environment as efficiently as humans is a fundamental problem in robotics.