An evading strategy for hypersonic vehicle against multiple interceptors via reinforcement learning

Abstract

This paper proposes an evading strategy based on reinforcement learning for the pursuit-evasion problem considering hypersonic vehicle dynamics (evader) and multiple interceptors (pursuers). Analytical solutions are hard to obtain, so a reinforcement learning method is adopted for the vehicle’s guidance commands in evasion. To cast the problem to a Markov-Decision-Process (MDP), we first establish the motion model involves multiple pursuers and one evader. Moreover, we choose their positions, and velocities change in the three-dimensional space as the state transition, considering a proportional navigation interception guidance. Furthermore, the weighted sum of the zero-effort-miss perpendicular to line-of-sight and the magnitude of maneuver is chosen to be a reward function. After training using a PPO-Clip algorithm, a policy for evasion is obtained. Numerical experiments validate that the method we put forward can be used in the sequential evasion decision against multiple pursuers for a hypersonic vehicle.