Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration

arXiv:2404.03869v1 Announce Type: new
Abstract: The rise of multi-agent systems, especially the success of multi-agent reinforcement learning (MARL), is reshaping our future across diverse domains like autonomous vehicle networks. However, MARL still faces significant challenges, particularly in achieving zero-shot scalability, which allows trained MARL models to be directly applied to unseen tasks with varying numbers of agents. In addition, real-world multi-agent systems usually contain agents with different functions and strategies, while the existing scalable MARL methods only have limited heterogeneity. …

abstract agent arxiv autonomous autonomous vehicle challenges collaboration cs.ai cs.lg cs.ma cs.ro cs.sy diverse domains eess.sy future however multi-agent networks reinforcement reinforcement learning scalability scalable success systems tasks type zero-shot