A DDPG agent learns the gains of a fixed three-loop missile autopilot; with a shaped reference command in the reward, it matches or outperforms classical gain scheduling in simulation.
Interpolation of observer state feed- back controllers for gain scheduling,
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A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design
A DDPG agent learns the gains of a fixed three-loop missile autopilot; with a shaped reference command in the reward, it matches or outperforms classical gain scheduling in simulation.