Planar evasive aircrafts maneuvers using reinforcement learning

Abstract

In this paper, the reinforcement learning technique is proposed to implement evasive strategies for aircrafts during engagement. A simplified point-mass model is used to describe the aircraft and the missile equations of motion. The missile follows the pure proportional navigation guidance (PPNG) law to attack the aircraft. Q-learning algorithm which is a form of reinforcement learning is suggested to learn the evasive maneuvers. The performance of the proposed approach is analyzed with numerical simulations. It is shown that the aircraft evades from a missile properly by reinforcement learning with bang-bang type action profiles. © 2013 Springer-Verlag.