Tactical intent-driven autonomous air combat behavior generation method
Abstract With the rapid development and deep application of artificial intelligence, modern air combat is incrementally evolving towards intelligent combat. Although deep reinforcement learning algorithms have contributed to dramatic advances in in air combat, they still face challenges such as poor...
Saved in:
| Main Authors: | Xingyu Wang, Zhen Yang, Shiyuan Chai, Jichuan Huang, Yupeng He, Deyun Zhou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01685-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An air combat maneuver decision-making approach using coupled reward in deep reinforcement learning
by: Jian Yang, et al.
Published: (2025-06-01) -
FedRoute: A Multi-Server Federated Meta-DRL Routing Scheme for Tactical Air-Ground WSNs
by: Andrews A. Okine, et al.
Published: (2025-01-01) -
Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications
by: Sinan Ibrahim, et al.
Published: (2024-01-01) -
Enhancing Automated Maneuvering Decisions in UCAV Air Combat Games Using Homotopy-Based Reinforcement Learning
by: Yiwen Zhu, et al.
Published: (2024-12-01) -
A Study on Autonomous Control of Underwater Manipulator Autonomous Operation Based on Deep Reinforcement Learning
by: LI Xinyang, et al.
Published: (2023-12-01)