Multi-Agent Deep Reinforcement Learning Framework for Efficient Aerial Wildfire Fighting ^†

The increasing severity of global wildfires requires advanced suppression strategies to mitigate impacts on the environment and human life. This work investigates the applicability of Multi-Agent Reinforcement Learning (MARL) to aerial wildfire suppression using the SoSID Toolkit, an agent-based grid simulation grounded in cellular-automata-based fire propagation. To enhance interpretability and support the reconstruction of learned tactics, this work introduces the Dual Decomposition Framework, providing a modular structure for both the reward function and the observation space. This design enables the systematic evaluation of individual components, allowing the identification of the elements most relevant to effective wildfire suppression. The learned MARL policy is compared against a heuristic strategy inspired by real-world firefighting practice. The reward analysis confirms that the Dual Decomposition Framework enhances transparency in agent behavior by analyzing the contribution of individual components. The experiments further show that the learned policy can outperform the heuristic approach in terms of burned-area reduction when fire spread sensitivity is low, demonstrating the potential of MARL for effective suppression strategies. However, performance declines as spread sensitivity increases, indicating limited generalization and signs of overfitting to training conditions. The findings suggest that approaches such as curriculum learning may improve robustness under faster-spreading fire dynamics.