Reinforcement Learning-Based Terminal Constrained Guidance Law for Air-to-Ground Missiles Based on Dimensionless Models
To tackle the terminal angle guidance conundrum in air-to-ground missile strikes,a reinforcement learning approach based on dimensionless modeling and terminal rewards is presented.Through establishing a dimensionless model from the flight dynamics of missiles,this method shrinks the size of the state and observation space in the reinforcement learning environment,enhancing the training efficiency for angle-constrained guidance.It adopts a reinforcement strategy based on terminal rewards that takes into account the accuracy of hits and attack angles,circumventing the reward sparsity problem in conventional reinforcement learning.Utilizing the deep deterministic policy gradient algorithm,it conducts guidance law training optimized for inputs in typical scenarios.Simulation outcomes indicate that this method surpasses existing ones in the accuracy of hits and attack angles,demands less overload,and effectively resolves the issues of high computational requirements and low efficiency of current reinforcement learning guidance techniques,thereby demonstrating its practical application potential.
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