In autonomous driving, traditional Computer Vision (CV) agents often struggle in unfamiliar situations due to biases in the training data. Deep Reinforcement Learning (DRL) agents address this by learning from experience and maximizing rewards, which helps them adapt to dynamic environments. However, ensuring their generalization remains challenging, especially with static training environments. Additionally, DRL models lack transparency, making it difficult to guarantee safety in all scenarios, particularly those not seen during training. To tackle these issues, we propose a method that combines DRL with Curriculum Learning for autonomous driving. Our approach uses a Proximal Policy Optimization (PPO) agent and a Variational Autoencoder (VAE) to learn safe driving in the CARLA simulator. The agent is trained using two-fold curriculum learning, progressively increasing environment difficulty and incorporating a collision penalty in the reward function to promote safety. This method improves the agent's adaptability and reliability in complex environments, and understand the nuances of balancing multiple reward components from different feedback signals in a single scalar reward function. Keywords: Computer Vision, Deep Reinforcement Learning, Variational Autoencoder, Proximal Policy Optimization, Curriculum Learning, Autonomous Driving.

本研究针对传统计算机视觉代理在不熟悉情况下的偏差问题，提出了一种结合深度强化学习和课程学习的方法来提升自动驾驶的适应性和安全性。通过在CARLA仿真器中使用近端策略优化代理和变分自编码器进行训练，逐步增加环境难度并在奖励函数中引入碰撞惩罚，从而显著改善了代理在复杂环境中的可靠性和适应能力。

CuRLA：基于课程学习的深度强化学习在自动驾驶中的应用