3 citations · 5 across the 3 of their papers we have counts for
4 papers
LOPT: Learning Optimal Pigovian Tax in Sequential Social Dilemmas
Yun Hua, Shang Gao, Wenhao Li +5
In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how othe…
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning
Longkang Li, Siyuan Liang, Zihao Zhu +3
The permutation flow shop scheduling (PFSS), aiming at finding the optimal permutation of jobs, is widely used in manufacturing systems. When solving large-scale PFSS problems, tra…
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…
VMAgent: Scheduling Simulator for Reinforcement Learning
Junjie Sheng, Shengliang Cai, Haochuan Cui +9
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling. VMAgent is inspired by practical virtua…