  The rapid rise of air pollution events necessitates accurate, real-time
monitoring for informed mitigation strategies. Data Assimilation (DA) methods
provide promising solutions, but their effectiveness hinges heavily on optimal
measurement locations. This paper presents a novel approach for air quality
mapping where autonomous drones, guided by a collaborative multi-agent
reinforcement learning (MARL) framework, act as airborne detectives. Ditching
the limitations of static sensor networks, the drones engage in a synergistic
interaction, adapting their flight paths in real time to gather optimal data
for Data Assimilation (DA). Our approach employs a tailored reward function
with dynamic credit assignment, enabling drones to prioritize informative
measurements without requiring unavailable ground truth data, making it
practical for real-world deployments. Extensive experiments using a real-world
dataset demonstrate that our solution achieves significantly improved pollution
estimates, even with limited drone resources or limited prior knowledge of the
pollution plume. Beyond air quality, this solution unlocks possibilities for
tackling diverse environmental challenges like wildfire detection and
management through scalable and autonomous drone cooperation.
