为什么自动驾驶中的强化学习，没有很好的落地？

Core Viewpoint - The article discusses the challenges of implementing reinforcement learning (RL) in the field of autonomous driving, particularly focusing on the issue of reward hacking and the balance between safety and efficiency [2][3]. Group 1: Challenges in Reinforcement Learning for Autonomous Driving - Reinforcement learning faces a significant issue known as reward hacking, where increasing safety requirements can lead to decreased efficiency, and vice versa [2]. - Designing a balanced reward system that can enhance overall performance in RL models is complex, as achieving equilibrium among multiple rewards is challenging [2]. - The application of RL in autonomous driving is complicated by the need to adhere to various driving rules during the driving process, unlike in embodied intelligence where the focus is primarily on local motion [2]. Group 2: Need for a Suitable Framework - A crucial factor for the successful implementation of RL in autonomous driving is the development of a robust architecture that can effectively integrate with RL [3]. - Existing models in autonomous driving are unlikely to be directly applicable to RL without significant modifications [3]. Group 3: Community and Resources - The "Autonomous Driving Knowledge Planet" community aims to provide a comprehensive platform for technical exchange and learning in the field of autonomous driving, with over 4,000 members [6][10]. - The community offers a variety of resources, including learning routes, technical discussions, and access to industry experts, to assist both beginners and advanced practitioners in the field [6][10].