4 papers
A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling
Ruisong Zhou, Haijun Zou, Li Zhou +2
Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computat…
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
Wenkang Ji, Huaben Chen, Mingyang Chen +6
The development of control policies for multi-robot systems traditionally follows a complex and labor-intensive process, often lacking the flexibility to adapt to dynamic tasks. Th…
Multi-Task Multi-Agent Reinforcement Learning via Skill Graphs
Guobin Zhu, Rui Zhou, Wenkang Ji +3
Multi-task multi-agent reinforcement learning (MT-MARL) has recently gained attention for its potential to enhance MARL's adaptability across multiple tasks. However, it is challen…
LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation
Guobin Zhu, Rui Zhou, Wenkang Ji +1
Although Multi-Agent Reinforcement Learning (MARL) is effective for complex multi-robot tasks, it suffers from low sample efficiency and requires iterative manual reward tuning. La…