Effect of Integrating Variational Autoencoder with SENet on Dimensionality Reduction in Driving Behavior Learning

Dimensionality reduction using Variational Autoencoder (VAE) is widely employed in learning diverse state representations, such as in autonomous driving tasks. Conventional VAE-based dimensionality reduction is a commonly used method for reducing the computational cost associated with learning from high-dimensional data, particularly image data, while achieving high performance. In this paper, we investigate the impact of integrating VAE with Squeeze-and-Excitation Networks (SENet), referred to as SENet-VAE, on the accuracy of learning driving behaviors in deep reinforcement learning. We conduct a series of experiments comparing three setups: raw image data, conventional VAE, and SENet-VAE. Additionally, we explore the effect of applying hyperparameters to the Kullback-Leibler (KL) divergence term in the objective function of the SENet-VAE to further optimize performance. Our results demonstrate that SENet-VAE outperforms the conventional VAE in terms of learning accuracy, with hyperparameter tuning leading to performance gains.