  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.
