Evaluation of Impact of Convolutional Neural Network-Based Feature Extractors on Deep Reinforcement Learning for Autonomous Driving

Reinforcement Learning (RL) enables learning optimal decision-making strategies by maximizing cumulative rewards.Deep reinforcement learning (DRL) enhances this process by integrating deep neural networks (DNNs) for effective feature extraction from highdimensional input data.Unlike prior studies focusing on algorithm design, we investigated the impact of different feature extractors, DNNs, on DRL performance.We propose an enhanced feature extraction model to improve control effectiveness based on the proximal policy optimization (PPO) framework in autonomous driving scenarios.Through a comparative analysis of well-known convolutional neural networks (CNNs), MobileNet, SqueezeNet, and ResNet, the experimental results demonstrate that our model achieves higher cumulative rewards and better control stability, providing valuable insights for DRL applications in autonomous systems.