A Rust-Implemented Dueling Double DQN for Fixed-Wing Autonomous Flight

Abstract

Deep reinforcement learning for autonomous unmanned aerial vehicle control has largely been demonstrated with multirotor platforms and high-level machine-learning frameworks. This study presents a Dueling Double Deep Q-Network (D3QN) training pipeline implemented in Rust without an external machine-learning library and integrated with Godot 4 through GDExtension for fixed wing flight control. The controller addresses fixed-wing requirements, including airspeed maintenance, lift management, throttle regulation, stall avoidance, and coordinated turning. The network combines a duelling architecture, double Q-learning, prioritised experience replay, and three-step returns in a 512→256 hidden-layer configuration containing 142,088 parameters for the 16-dimensional input case. The agent selects among seven discrete actions and supports both 12-dimensional and 16-dimensional observation spaces through a cross-dimensional weight-transfer procedure. Training was conducted for 200 episodes using four random seeds. Across seeds, the mean best episodic reward was 4825±40, while the coefficient of variation for best reward was 0.8%. In the final 30 episodes, no crashes were recorded, although completion rates varied substantially between seeds. Airspeed remained within ±8 m/s of the 50 m/s target. Batch-64 gradient updates required less than 1 ms, representing an approximately 35-fold reduction in latency relative to the preceding GDScript implementation, and the reported runtime memory footprint remained below 50 MB. These findings support the feasibility of native Rust-based D3QN training for real-time fixed-wing simulation, while the observed inter-seed variability indicates that reward shaping and convergence robustness require further evaluation.