Optimizing Constrained Guidance Policy With Minimum Overload Regularization

Weilin Luo

doi:10.1109/TCSI.2022.3163463

Optimizing Constrained Guidance Policy With Minimum Overload Regularization

2022-07-01

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AI

DOI：10.1109/TCSI.2022.3163463

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Using reinforcement learning (RL) algorithm to optimize guidance law can address non-idealities in complex environment. However, the optimization is difficult due to huge state-action space, unstable training, and high requirements on expertise. In this paper, the constrained guidance policy of a neural guidance system is optimized using improved RL algorithm, which is motivated by the idea of traditional model-based guidance method. A novel optimization objective with minimum overload regularization is developed to restrain the guidance policy directly from generating redundant missile maneuver. Moreover, a bi-level curriculum learning is designed to facilitate the policy optimization. Experiment results show that the proposed minimum overload regularization can reduce the vertical overloads of missile significantly, and the bi-level curriculum learning can further accelerate the optimization of guidance policy.

Keywords:

Optimization

Missile guidance

Reinforcement learning

Training

Three-dimensional displays

Neural networks

Task analysis

Missile guidance

reinforcement learning

minimum overload regularization

curriculum learning

Optimizing Constrained Guidance Policy With Minimum Overload Regularization

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