Deep Reinforcement Learning for AutonomousDrone Navigation: Application to Forest FireMonitoring

Forest fires represent a critical global challenge, inflicting extensive ecological,infrastructural, and human damage. The imperative for early detection andrapid response is paramount for effective management and mitigation strategies.This report focuses on leveraging autonomous Drones as a promising solutionfor forest fire surveillance. The study investigates the optimization of DRONEsnavigation and surveillance capabilities through the application of Deep Rein-forcement Learning (DRL). A comparative analysis of the Deep Q-Network(DQN) and Q-learning algorithms is presented within the realistic AirSim sim-ulation environment. The findings demonstrate DQN’s superior performance incomplex, dynamic scenarios, particularly in trajectory optimization, obstacleavoidance, and efficient fire detection. The ability of DRL to learn complex poli-cies from raw sensory input, such as images, makes it uniquely suited to theinherent unpredictability and high dimensionality of real-world fire scenarios,where explicit programming of all contingencies is impractical. This research con-tributes significantly to enhancing early detection, improving response times, andrefining firefighting strategies by providing a robust and adaptive autonomoussurveillance system.Keywords: Deep Reinforcement Learning (DRL), Autonomous Navigation, DRONEs,Drones, Forest Fire Surveillance, Early Detection, AirSim Simulator, Q-learning, DeepQ-Network (DQN), Obstacle Avoidance, Trajectory Optimization, EfficientExploration