Planar Evasive Aircrafts Maneuvers Using Reinforcement Learning
In this paper, the reinforcement learning technique is proposed to implement evasive strategies for aircrafts during engagement. A simplified point-mass model is used to describe the aircraft and the missile equations of motion. The missile follows the pure proportional navigation guidance (PPNG) law to attack the aircraft. Q-learning algorithm which is a form of reinforcement learning is suggested to learn the evasive maneuvers. The performance of the proposed approach is analyzed with numerical simulations. It is shown that the aircraft evades from a missile properly by reinforcement learning with bang-bang type action profiles.
Keywordsmissile evasive maneuvers reinforcement Leraning Q-learning pure proportional navigation
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- 4.Watkins, C.: Learning from delayed rewards, PhD. Thesis, Cambridge, England (1989)Google Scholar
- 5.Manju, M.S.: An analysis of Q-learning algorithms with strategies of reward function. International Journal on Computer Science and Engineering 3(2), 814–820 (2011)Google Scholar
- 8.Sutton, R.S., Barto, A.G.: Reinforcement Learning. The MIT Press (1998)Google Scholar
- 9.Lee, D., Bang, H., Baek, K.: Autorotation of an Unmanned Helicopter by a Reinforcement Learning Algorithm. In: AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hawaii (2008)Google Scholar
- 10.Jung, B., Kim, K.S., Kim, Y.: Guidance law for evasive aircraft maneuvers using artificial intelligence. In: Guidance, Navigation, and Control Conference and Exhibit, Austin, Texas (2003)Google Scholar