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Trajectory Planning for Hypersonic Vehicles with Reinforcement Learning

This paper discusses the problem of avoiding threats during the cruising flight of hypersonic vehicles (HV). Considering the constraints on kinematics of HV and changing environments, this paper proposes two methods of trajectory planning that taking the overload or the rotational angular velocity of the ballistic deflection angle as actions of agent. Meanwhile, the agent’s policy is optimized with policy gradient method, and Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are used for comparison. Experimental results show that PPO and SAC have similar performance in penetration missions. Moreover, in the complicated flight environment, the method of taking overload and exploration distance as actions has a higher penetration success ratio.