A pretrained proximal policy optimization algorithm with reward shaping for aircraft guidance to a moving destination in three-dimensional continuous space

To enhance the performance of guiding an aircraft to a moving destination in a certain direction in three-dimensional continuous space, it is essential to develop an efficient intelligent algorithm. In this article, a pretrained proximal policy optimization (PPO) with reward shaping algorithm, which does not require an accurate model, is proposed to solve the guidance problem of manned aircraft and unmanned aerial vehicles. Continuous action reward function and position reward function are presented, by which the training speed is increased and the performance of the generated trajectory is improved. Using pretrained PPO, a new agent can be trained efficiently for a new task. A reinforcement learning framework is built, in which an agent can be trained to generate a reference trajectory or a series of guidance instructions. General simulation results show that the proposed method can significantly improve the training efficiency and trajectory performance. The carrier-based aircraft approach simulation is carried out to prove the application value of the proposed approach.

Automated summary

A pretrained PPO algorithm is proposed to solve the guidance problem of manned aircraft and unmanned aerial vehicles, with continuous action reward function and position reward function to increase training speed and trajectory performance.