| Autor: |
Wenlin Chen, Shuai Wang, Chong Jiang, Siyu Wang, Lina Hao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 83502-83512 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3409616 |
| Popis: |
This paper proposes an unmanned combat decision-making algorithm based on PPO and expert systems. The experimental results show that the algorithm has good decision-making ability. A strategy optimization method based on a self-encoding neural network is proposed, which greatly improves the effective decision-making rate of the original algorithm. In view of the opaque problem of the unmanned combat decision-making model obtained by the above deep reinforcement learning algorithm, a local interpretability algorithm GLIME based on Generative Adversarial Network (GAN) and Local interpretable model-agnostic explanations (LIME) is proposed, which improves the stability of the LIME algorithm. Finally, combined with the global interpretability algorithm, Permutation Feature Importance (PFI), the decision-making samples are analyzed from both local and global perspectives, providing comprehensive and stable explanations for the decision-making algorithm, thereby improving the transparency of the decision-making algorithm. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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