A Decision-Making Framework for Task Allocation in Single-Pilot Operations: Synergistic Empowerment Mechanisms of Reinforcement Learning and Deep Learning

Abstract

The task allocation decision-making architecture for Single Pilot Operation (SPO) represents a critical development direction in aviation to address soaring operational costs and the global pilot shortage. This paper systematically reviews the synergistic enabling mechanisms of Reinforcement Learning (RL) and Deep Learning (DL) within this architecture. DL serves as the perceptual foundation, processing visual information via CNNs and optimizing human-machine interaction through Transformers to achieve efficient multimodal data comprehension and situational awareness. RL functions as the decision core, leveraging methods such as multi-agent Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) to model complex task allocation problems as Markov Decision Processes, enabling dynamic resource scheduling and multi-constraint optimization. Through deep integration in a “perception-decision-optimization” closed-loop, this dual approach significantly enhances the SPO system’s responsiveness, robustness, and safety in high-real-time, high-uncertainty environments. This collaborative mechanism provides critical theoretical foundations and technical pathways for developing next-generation intelligent aviation systems compliant with airworthiness standards and enabling efficient human-machine collaboration.