activity
20172024
most citedRevisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning

46 citations · 57 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2024★ 6 cited

Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy

Zhenyu Guan, Xiangyu Kong, Fangwei Zhong +1

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation,…

cs.AI2024

CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents

Siyuan Qi, Shuo Chen, Yexin Li +11

The generalization of decision-making agents encompasses two fundamental elements: learning from past experiences and reasoning in novel contexts. However, the predominant emphasis…

cs.SD2023★ 1 cited

DasFormer: Deep Alternating Spectrogram Transformer for Multi/Single-Channel Speech Separation

Shuo Wang, Xiangyu Kong, Xiulian Peng +3

For the task of speech separation, previous study usually treats multi-channel and single-channel scenarios as two research tracks with specialized solutions developed respectively…

cs.SD2022

Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation

Xiaoyu Wang, Xiangyu Kong, Xiulian Peng +1

In this paper we propose a multi-modal multi-correlation learning framework targeting at the task of audio-visual speech separation. Although previous efforts have been extensively…

cs.CV2020★ 3 cited

Pose-Assisted Multi-Camera Collaboration for Active Object Tracking

Jing Li, Jing Xu, Fangwei Zhong +3

Active Object Tracking (AOT) is crucial to many visionbased applications, e.g., mobile robot, intelligent surveillance. However, there are a number of challenges when deploying act…

cs.AI2017★ 46 cited

Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning

Xiangyu Kong, Bo Xin, Fangchen Liu +1

Many tasks in artificial intelligence require the collaboration of multiple agents. We exam deep reinforcement learning for multi-agent domains. Recent research efforts often take…

cs.CV2017★ 1 cited

Collaborative Deep Reinforcement Learning for Joint Object Search

Xiangyu Kong, Bo Xin, Yizhou Wang +1

We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under intera…