activity
20192023
most citedLearning Structured Communication for Multi-agent Reinforcement Learning

7 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation

Chuyun Shen, Wenhao Li, Ya Zhang +1

The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high…

cs.RO20221 cited

Multi-Agent Path Finding with Prioritized Communication Learning

Wenhao Li, Hongjun Chen, Bo Jin +3

Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world problems, e.g., automation warehouses. The learning-based, fully decentralized framework has bee…

cs.LG20211 cited

Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning

Wenhao Li, Xiangfeng Wang, Bo Jin +3

When solving a complex task, humans will spontaneously form teams and to complete different parts of the whole task, respectively. Meanwhile, the cooperation between teammates will…

cs.LG20207 cited

Learning Structured Communication for Multi-agent Reinforcement Learning

Junjie Sheng, Xiangfeng Wang, Bo Jin +5

This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We summarize the general categories of topology fo…

cs.CV2019

Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning

Xuan Liao, Wenhao Li, Qisen Xu +5

Existing automatic 3D image segmentation methods usually fail to meet the clinic use. Many studies have explored an interactive strategy to improve the image segmentation performan…