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Air combat maneuver decision based on deep reinforcement learning with auxiliary reward

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Abstract

For air combat maneuvering decision, the sparse reward during the application of deep reinforcement learning limits the exploration efficiency of the agents. To address this challenge, we propose an auxiliary reward function considering the impact of angle, range, and altitude. Furthermore, we investigate the influences of the network nodes, layers, and the learning rate on decision system, and reasonable parameter ranges are provided, which can serve as a guideline. Finally, four typical air combat scenarios demonstrate good adaptability and effectiveness of the proposed scheme, and the auxiliary reward significantly improves the learning ability of deep Q network (DQN) by leading the agents to explore more intently. Compared with the original deep deterministic policy gradient and soft actor critic algorithm, the proposed method exhibits superior exploration capability with higher reward, indicating that the trained agent can adapt to different air combats with good performance.

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Data availability

No datasets were analyzed during the current study. The data generated during the current study are available from the corresponding author on reasonable request.

Abbreviations

UCAV:

Unmanned combat aerial vehicle

DQN:

Deep Q network

DDPG:

Deep deterministic policy gradient

SAC:

Soft actor critic

LOS:

Line of sight

AA:

Aspect angle (rad)

ATA:

Antenna train angle (rad)

HTC:

Heading crossing angle (rad)

R :

Distance between UCAV and target (m/s)

\(\dot{R}\) :

Change rate of R (m/s)

\({z_\text{U}}\),\({z_\text{T}}\) :

Flight altitude of UCAV and target (m)

\({v_\text{U}}\),\({v_\text{T}}\) :

Flight speed of UCAV and target (m/s)

\(\Delta h\) :

Flight altitude difference (rad)

\(\Delta v\) :

Flight speed difference (m/s)

\({R_\text{w}}\) :

Airborne weapon attack range (m)

\({\varphi _\text{w}}\) :

Maximum weapon attack angle (deg)

\({q_\text{w}}\) :

Maximum aspect angle

\({P_\text{U}},{P_\text{T}}\) :

Position vector of UCAV and target

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Acknowledgements

This work was funded by the National Nature Science Foundation of China Grant Nos. 62073177, 61973175, 62003351.

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Authors and Affiliations

  1. College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Tingyu Zhang, Yongshuai Wang, Mingwei Sun & Zengqiang Chen

  2. Key Laboratory of Intelligent Robotics of Tianjin, Tianjin, 300350, China

    Yongshuai Wang, Mingwei Sun & Zengqiang Chen

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  1. Tingyu Zhang
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Correspondence to Yongshuai Wang.

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Appendix

Appendix

Algorithm 1
figure a

Maneuver decision algorithm based on the DQN

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Zhang, T., Wang, Y., Sun, M. et al. Air combat maneuver decision based on deep reinforcement learning with auxiliary reward. Neural Comput & Applic 36, 13341–13356 (2024). https://doi.org/10.1007/s00521-024-09720-z

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