Nowadays, autonomous vehicles are gaining traction due to their numerous potential applications in resolving a variety of other real-world challenges. However, developing autonomous vehicles need huge amount of training and testing before deploying it to real world. While the field of reinforcement learning (RL) has evolved into a powerful learning framework to the development of deep representation learning, and it is now capable of learning complicated policies in high-dimensional environments like in autonomous vehicles. In this regard, we make an effort, using Deep Q-Learning, to discover a method by which an autonomous car may maintain its lane at top speed while avoiding other vehicles. After that, we used CARLA simulation environment to test and verify our newly acquired policy based on the problem formulation.

翻译：如今，自动驾驶车辆因在解决多种现实世界挑战中具有广泛潜在应用而备受关注。然而，开发自动驾驶车辆需要大量训练与测试才能部署至真实环境。随着强化学习（RL）已发展成为深度表征学习的强大学习框架，它现在能够学习高维环境（如自动驾驶车辆）中的复杂策略。为此，我们尝试利用深度Q学习算法，探索一种使自动驾驶汽车在保持最高车速的同时避开其他车辆的车道保持方法。随后，我们基于问题构建，使用CARLA仿真环境对所获得的策略进行了测试验证。