攻击角度约束下的分布式强化学习制导方法

为提高导弹在攻击角度约束下对目标的打击效能，提出了一种基于深度确定性策略梯度算法的分布式强化学习制导策略。为了最大限度地减小攻击角度误差，设计了一种新的奖励函数，使导弹在满足视场角约束的同时，视线角向期望值收敛。此外，为了增强强化学习模型的泛化能力，提出了一种分布式探索策略，提高了模型训练过程中对环境的探索效率。仿真结果验证了所提出的分布式强化学习制导方法能够在固定攻击角度约束下实现对目标的精准打击。与传统制导律相比，所提制导方法的攻击角度误差更小，收敛速度更快。

A Distributed Reinforcement Learning Guidance Method under Impact Angle Constraints

In order to improve the target hitting effect of missile with the impact angle fixed, a distributed reinforcement learning guidance strategy based on deep deterministic policy gradient algorithm is proposed. To minimize the impact angle error, a new reward function is designed to make the line of sight angle converge to the expected value while meeting the field of view angle constraint. In addition, in order to enhance the generalization ability of the reinforcement learning model, a distributed exploration strategy is proposed to improve the efficiency of environment exploration during model training. The simulation results verify that the proposed distributed reinforcement learning guidance method can achieve accurate attack on the target under the constraint of fixed impact angle. Compared with the traditional guidance law, the impact angle error of the proposed guidance law is smaller and the convergence rate is faster.