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- W4285282132 abstract "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." @default.
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- W4285282132 date "2022-07-01" @default.
- W4285282132 modified "2023-10-16" @default.
- W4285282132 title "Optimizing Constrained Guidance Policy With Minimum Overload Regularization" @default.
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- W4285282132 doi "https://doi.org/10.1109/tcsi.2022.3163463" @default.
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