Survivability Based Optimal Air Combat Mission Planning with Reinforcement Learning

摘要

The mission-threat analysis is the key factor in air combat missions. The strategic success evaluation of a mission is done through survivability assessment. The mission assessment process begins with the modeling of threats in which a hostile environment can be constructed through these models. Then, based on the environment model including stochasticity, an optimum strategy maximizing the aircraft's survivability is obtained. This study proposes a survivability analysis and optimal mission planning methodology using reinforcement learning for an aircraft flying in a human-made and hostile natural environment.