An event-triggered deep reinforcement learning guidance algorithm for intercepting maneuvering target of speed...

Hypersonic vehicle interception raises stringent and challenging requirements for traditional guidance laws in terms of speed and maneuverability advantage. Deep reinforcement learning algorithms provide potential solutions for intercepting maneuvering targets of speed advantage, but they are greatly hindered by policy training inefficiency. To address these limitations, we propose a novel event-triggered deep reinforcement learning (ETDRL) algorithm along with an event-triggered training and time-triggered execution (ETTE) framework. The ETTE framework reformulates the agent-environment interaction as an event-triggered Markov decision process (ETMDP) model, where the agent updates its action only when the environment state meets a specific event triggering condition, otherwise maintaining the previous behavior between events. As a result, this approach significantly accelerates policy training by reducing the total number of decision steps required during the learning phase. To mitigate the potential degradation in control performance caused by the event-triggered mechanism, the ETTE framework enables well-trained policies to be executed with a fixed decision interval, that is, in a time-triggered way. Based on the proposed method, an ETDRL guidance law is developed for intercepting maneuvering targets of speed advantage under constraints of limited maneuverability, large initial heading error, and bearings-only measurement. By following the design principle of nullifying the line-of-sight angular rate to establish a collision course with the target, we model the guidance problem as an ETMDP. The twin delayed deep deterministic policy gradient algorithm is utilized to train the ETDRL guidance law. Numerical simulations demonstrate the superiority of the proposed ETDRL method over DRL algorithms in terms of policy training efficiency, while also highlighting its enhanced guidance performance over traditional methods.