Autonomous Air Combat with Reinforcement Learning under Different Noise Conditions

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.