Autonomous Air Combat with Reinforcement Learning under Different Noise Conditions Farkli Gürültü Şartlari Altinda Pekiştirmeli Öǧrenme ile Otonom Hava Muharebesi

Taşbaş A. S., Serbest S., Şahin S. O., Üre N. K.

31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, İstanbul, Turkey, 5 - 08 July 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu59756.2023.10224036
  • City: İstanbul
  • Country: Turkey
  • Keywords: air combat, decision-making, neural networks, reinforcement learning
  • Istanbul Technical University Affiliated: Yes


The autonomous realization of air combat with reinforcement learning-based methods has recently become a prominent field of study. In this paper, we present a classifier architecture to solve the air combat problem in noisy environments, which is a sub-branch of this field. We collect data from environments with different noise levels using air combat simulation. Using these data, we train three different data sets with the number of state stacks 2, 4, and 8. We train neural network-based classifiers using these datasets. These classifiers adaptively estimate the noise level in the environment at each time step and activate the appropriate pre-trained reinforcement learning policy based on this estimate. In addition, we share the performance comparison of these classifiers in different state stacks.