An Integrated MADQN–Heuristic Framework for Swarm Robotic Fire Detection and Extinguishing

Wildfires pose a growing global threat, demanding rapid, scalable, and autonomous response strategies. This study proposes HG-MADQN (Heuristic-Guided Multi-Agent Deep Q-Network), a hybrid framework that integrates reinforcement learning with biologically inspired pheromone-based heuristics to achieve adaptive fire detection and suppression using drone swarms. The system models a decentralized swarm operating in a grid-based environment, where each drone combines learned policies with heuristic guidance derived from a dual-pheromone mechanism (a fire-attraction field guiding suppression and a coverage-repulsion field promoting exploration). The proposed hybrid approach ensures efficient coordination, minimizes redundant movements, and maintains continuous area coverage without centralized control. Simulation experiments conducted on dynamic wildfire scenarios demonstrate that HG-MADQN significantly outperforms traditional heuristic, Lévy-Flight, and reinforcement learning (MADQN) algorithms. It achieves faster containment, reduced burned area, and lower resource consumption, while exhibiting strong robustness across multiple swarm sizes and fire configurations. The results confirm that hybridizing learned and heuristic decision models enables a balanced exploration–exploitation trade-off, leading to improved scalability and resilience in cooperative fire suppression missions.