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...
Saved in:
| Main Authors: | Jianqi Wang, Shengtao Long, Su Wang, Kaiyu Zhan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/ijae/9542576 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Observer-Based Deep Reinforcement Learning for Robust Missile Guidance and Control
by: Wenwen Wang, et al.
Published: (2025-01-01) -
Generalized ESO and Predictive Control Based Robust Autopilot Design
by: Bhavnesh Panchal, et al.
Published: (2016-01-01) -
A nonlilear controller for ship autopilots
by: Le Thanh Tung
Published: (2012-09-01) -
Reward shaping based reinforcement learning for intelligent missile penetration attack strategy planning
by: LUO Junren, et al.
Published: (2024-06-01) -
Adaptive Twisting Sliding Control for Integrated Attack UAV’s Autopilot and Guidance
by: Nguyen Minh Tu, et al.
Published: (2025-01-01)