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 utilization of attention mechanisms plays a key role in our model. It allows for the effective processing of dynamic context data and nuanced agent interactions, leading to more refined decision-making. Applied in standard MARL scenarios, such as the Stanford Intelligent Systems Laboratory (SISL) Pursuit and Multi-Particle Environments (MPE) Simple Spread, our method has been shown to improve both learning efficiency and the effectiveness of collaborative behaviors. The results indicate that our attention-based approach can be a viable approach for improving the efficiency of MARL training process, integrating domain-specific knowledge at the action level.

翻译：本文提出一种通过整合领域知识与基于注意力的策略机制来增强多智能体强化学习（MARL）的新方法。该方法聚焦于将领域特定知识融入学习过程，从而简化协作行为的开发。通过使智能体专注于复杂任务的关键方面以优化学习曲线，该方法旨在降低MARL通常存在的复杂性和学习开销。注意力机制的应用在模型中扮演关键角色，能够有效处理动态上下文数据和智能体间微妙的交互，从而形成更精细的决策。在斯坦福智能系统实验室（SISL）追逐（Pursuit）和多粒子环境（MPE）简单扩散（Simple Spread）等标准MARL场景中实验验证，本方法能同时提升学习效率与协作行为的有效性。结果表明，基于注意力的方法可通过在动作层面整合领域特定知识，成为提升MARL训练过程效率的可行方案。