Research on Multi-Task Learning Optimization Models for Intelligent Driving

This study proposes an advanced autonomous driving model named MVWG-PPO, which integrates meta-learning with reinforcement learning to optimize both feature extraction and decision-making processes. The proposed model aims to enhance the overall performance of end-to-end autonomous driving systems, addressing the challenges associated with feature extraction and reinforcement learning. The MVWG-PPO model builds upon existing technologies such as Variational Autoencoders (VAE), Wasserstein Generative Adversarial Networks (WGAN), and Proximal Policy Optimization (PPO). By integrating meta-learning strategies, the model reduces training times and improves the quality of extracted features, while refining the reward function in PPO to boost the training performance.