Autonomous-Driving-in-Carla-using-Deep-Reinforcement-Learning
This project uses CARLA simulation and Deep Reinforcement Learning with Proximal Policy Optimization to improve autonomous driving capabilities. It trains agents in hyper-realistic urban environments, leveraging a Variational Autoencoder for efficient learning. By focusing on continuous state and action spaces, it aims to provide reliable autonomous navigation on predetermined routes, offering a comprehensive end-to-end driving solution.
