Zobrazeno 1 - 10
of 175
pro vyhledávání: '"ZHANG Wanpeng"'
Publikováno v:
智能科学与技术学报, Vol 6, Pp 301-318 (2024)
As a new paradigm in the field of artificial intelligence, game reinforcement learning is an advanced mainstream method to solve the edge computing problem of low-orbit constellation. The multi-agent deep reinforcement learning integrated into the ga
Externí odkaz:
https://doaj.org/article/aa33c7d9789d453ebc083a97d4a1fa42
Publikováno v:
智能科学与技术学报, Vol 6, Pp 189-200 (2024)
Facing the future requirements of distributed warfare at sea, the strategic planning of missile penetration is firstly analyzed based on the background of intelligent missile salvo penetration against surface ships in distributed warfare scenario. Se
Externí odkaz:
https://doaj.org/article/25905f46478340b0bd20d0d06ceaa673
Publikováno v:
智能科学与技术学报, Vol 5, Pp 313-329 (2023)
Multi-agent systems are a cutting-edge research concept in the field of distributed artificial intelligence. Traditional multi-agent reinforcement learning methods mainly focus on topics such as group behavior emergence, multi-agent cooperation and c
Externí odkaz:
https://doaj.org/article/2dfebd73d09240c08860a101d8e77cf9
Publikováno v:
Meikuang Anquan, Vol 53, Iss 2, Pp 142-148 (2022)
To accurately detect fault by in-seam seismic survey, 10 typical observation system models, belonged to 3 categories of working face interior detection, exterior detection and others, have been built generally according to location, fault strike and
Externí odkaz:
https://doaj.org/article/909c9699ffbc43b28065d524a226311c
We present VideoOrion, a Video Large Language Model (Video-LLM) that explicitly captures the key semantic information in videos--the spatial-temporal dynamics of objects throughout the videos. VideoOrion employs expert vision models to extract object
Externí odkaz:
http://arxiv.org/abs/2411.16156
Autor:
Zhang, Wanpeng, Xie, Zilong, Feng, Yicheng, Li, Yijiang, Xing, Xingrun, Zheng, Sipeng, Lu, Zongqing
Multimodal Large Language Models have made significant strides in integrating visual and textual information, yet they often struggle with effectively aligning these modalities. We introduce a novel image tokenizer that bridges this gap by applying t
Externí odkaz:
http://arxiv.org/abs/2410.02155
Autor:
Zhang, Wanpeng, Lu, Zongqing
Large Language Models (LLMs) have demonstrated significant success across various domains. However, their application in complex decision-making tasks frequently necessitates intricate prompt engineering or fine-tuning, leading to challenges in unsee
Externí odkaz:
http://arxiv.org/abs/2309.17176
In real-world scenarios, the application of reinforcement learning is significantly challenged by complex non-stationarity. Most existing methods attempt to model changes in the environment explicitly, often requiring impractical prior knowledge of e
Externí odkaz:
http://arxiv.org/abs/2306.02747
Autor:
Zhang, Wanpeng1,2 (AUTHOR) bsgzwp@cnpc.com.cn, Xie, Hang1,2 (AUTHOR) bsgxh03@cnpc.com.cn, Yu, Xiaoquan3 (AUTHOR) yuxiaoquangood@163.com, Zhang, Jingang1,2 (AUTHOR) bsgzjg@cnpc.com.cn, Zhou, Chao1,2 (AUTHOR) bsgzc2@cnpc.com.cn, Song, Hongbing1,2 (AUTHOR) bsgshb01@cnpc.com.cn, Huang, Jiankang4 (AUTHOR) yuxiaoquangood@163.com
Publikováno v:
Materials (1996-1944). Nov2024, Vol. 17 Issue 22, p5634. 18p.
We investigate the use of natural language to drive the generalization of policies in multi-agent settings. Unlike single-agent settings, the generalization of policies should also consider the influence of other agents. Besides, with the increasing
Externí odkaz:
http://arxiv.org/abs/2210.13942