activity
20182022
most citedGCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning

25 citations · 29 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG20224 cited

Contextual Transformer for Offline Meta Reinforcement Learning

Runji Lin, Ye Li, Xidong Feng +6

The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and computer vision tasks. However, such a paradigm is…

cs.LG2022

Learning to Identify Top Elo Ratings: A Dueling Bandits Approach

Xue Yan, Yali Du, Binxin Ru +3

The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluati…

cs.MA202225 cited

GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning

Jingqing Ruan, Yali Du, Xuantang Xiong +6

Many real-world scenarios involve a team of agents that have to coordinate their policies to achieve a shared goal. Previous studies mainly focus on decentralized control to maximi…

cs.MA2019

Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning

Liheng Chen, Hongyi Guo, Yali Du +7

In many real-world problems, a team of agents need to collaborate to maximize the common reward. Although existing works formulate this problem into a centralized learning with dec…

cs.MA2019

Bi-level Actor-Critic for Multi-agent Coordination

Haifeng Zhang, Weizhe Chen, Zeren Huang +4

Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the…

cs.AI2018

Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation

Haifeng Zhang, Zilong Guo, Han Cai +5

With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume. Fo…