Research on the Design and Simulation of Missile Intelligent Agent Autopilot Integrated With Deep Reinforcement Learning
This paper proposes an innovative method for missile autopilot design based on the deep deterministic policy gradient (DDPG) algorithm. Under the framework of deep reinforcement learning, by integrating the missile’s dynamic characteristics to optimize the reward function and network structure, an a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/ijae/9542576 |
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| Summary: | This paper proposes an innovative method for missile autopilot design based on the deep deterministic policy gradient (DDPG) algorithm. Under the framework of deep reinforcement learning, by integrating the missile’s dynamic characteristics to optimize the reward function and network structure, an adaptive control model capable of adapting to complex flight conditions was constructed. The effective integration of the autopilot and guidance system was achieved in the Simulink simulation environment, and the model’s validity and robustness in complex dynamic environments were verified. This research provides a new technological approach for the intelligent design of missile autopilots. |
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| ISSN: | 1687-5974 |