关于深度强化学习方法在航空装备维修保障中应用的可行性分析
Feasibility Analysis on the Application of Deep Reinforcement Learning Method in Aviation Equipment Maintenance

当前，航空装备维修保障策略主要依靠于人工决策，对于航空装备寿命梯次编排、组训任务规划、机务保障工作规划等航空装备维修保障工作决策存在一定的局限性和短视性。本文通过对典型深度强化学习方法进行分析，围绕某飞行训练部队航空装备维修保障策略智能化开展可行性分析，建立基于多智能体的航空装备维修保障深度强化学习模型，为后续完成航空装备维修保障环境建模、多智能体强化学习训练等工作提供理论支持，对航空兵建立科学、高效的航空装备维修保障策略，实现智能化、科学化的维修保障方式，具有十分重要的现实意义。
Currently, aviation equipment maintenance strategies primarily rely on manual decision-making, which exhibits certain limitations and short-sightedness in tasks such as lifecycle scheduling, training mission planning, and maintenance support planning. This paper analyzes typical deep reinforcement learning methods, conducts a feasibility analysis on the intelligent development of aviation equipment maintenance and support strategies for a flight training unit, and establishes a multi-agent-based deep reinforcement learning model for aviation equipment maintenance and support. It provides theoretical support for subsequent tasks such as environmental modeling for aviation equipment maintenance and multi-agent reinforcement learning training. This study holds significant practical importance for establishing scientific and efficient maintenance and support strategies for aviation forces, enabling intelligent and scientifically optimized maintenance approaches.