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SCIENCE CHINA Information Sciences, Volume 61, Issue 11: 119204(2018) https://doi.org/10.1007/s11432-018-9463-x

Missile aerodynamic design using reinforcement learning and transfer learning

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  • ReceivedFeb 28, 2018
  • AcceptedMay 3, 2018
  • PublishedSep 17, 2018

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61603210) and Aeronautical Science Foundation of China (Grant No. 20160758001).


Supplement

Appendix A.


References

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  • Figure 1

    (a) Design parameters of the missile; (b) architecture of the deep learning approach for aerodynamic design; protect łinebreak (c) performance comparison of NCGA, NSGA-II, MOPSO, DDPG, and SL-DDPG in the hybrid environment.

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