Abstract
Reinforcement learning is generating considerable interest in terms of building guidance law and solving optimization problems that were previously difficult to solve. Since reinforcement learning-based guidance laws often show better robustness than a previously optimized algorithm, several studies have been carried out on the subject. This paper presents a new approach to training missile guidance law by reinforcement learning and introducing some notable characteristics. The novel missile guidance law shows better robustness to the controller-model compared to the proportional navigation guidance. The neural network in this paper has identical inputs with proportional navigation guidance, which makes the comparison fair, distinguishing it from other research. The proposed guidance law will be compared to the proportional navigation guidance, which is widely known as quasi-optimal of missile guidance law. Our work aims to find effective missile training methods through reinforcement learning, and how better the new method is. Additionally, with the derived policy, we contemplated which is better, and in which circumstances it is better. A novel methodology for the training will be proposed first, and the performance comparison results will be continued therefrom.
Highlights
The proportional navigation guidance (PNG) is known as a quasi-optimal of interception guidance law
Reinforcement learning-based missile guidance law surprisingly works well on the noisy stochastic world, possibly because its algorithm is based on probability
The simulation result here implies that the Reinforcement learning (RL)-based missile guidance law is able to replace the PNG
Summary
The proportional navigation guidance (PNG) is known as a quasi-optimal of interception guidance law. Gaudet [3] had suspected an RL-based guidance law may help the logic itself to become robust He proposed an RL framework that has the ability to build the guidance law for the homing phase. Reinforcement learning-based missile guidance law surprisingly works well on the noisy stochastic world, possibly because its algorithm is based on probability. He brought PNG and Enhanced PNG as the comparison targets and showed that the RL generated guidance law has. In [9], Gurfil presents a guidance law that works well even in an environment with missile parametric uncertainties He focused on building a universal missile guidance law that even covers missiles having different systems.
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