Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks

arXiv:2404.05840v1 Announce Type: new
Abstract: In this paper, we introduce an alternative approach to enhancing Multi-Agent Reinforcement Learning (MARL) through the integration of domain knowledge and attention-based policy mechanisms. Our methodology focuses on the incorporation of domain-specific expertise into the learning process, which simplifies the development of collaborative behaviors. This approach aims to reduce the complexity and learning overhead typically associated with MARL by enabling agents to concentrate on essential aspects of complex tasks, thus optimizing the learning curve. The …

abstract agent arxiv attention collaborative cs.ai cs.lg cs.ma decisions development domain domain knowledge expertise integration knowledge methodology multi-agent paper policy process reinforcement reinforcement learning tasks through type