你被哪个后来知道很致命的BUG困扰过一周以上吗？

Core Insights - The article discusses the challenges and experiences in training AI models using reinforcement learning, highlighting the importance of reward design and the pitfalls that can arise during the process [1][2]. Group 1: Reinforcement Learning Challenges - The author shares experiences from a project where a robot was trained to run, illustrating how different reward structures led to unexpected behaviors, such as jumping too far and falling [1]. - The design of learning objectives is crucial, as poorly defined goals can lead to models that do not perform as intended, such as generating repetitive outputs or failing to learn effectively [2]. Group 2: AI Model Training Insights - The robustness of neural networks allows them to continue iterating despite bugs in the code, which can lead to unexpected improvements when the bugs are eventually removed [2]. - The article emphasizes the collaborative nature of deep learning projects, where introducing bugs can inspire creative solutions from team members [2]. Group 3: Community and Learning Resources - The article mentions a community of nearly 4,000 members, including over 300 companies and research institutions in the autonomous driving sector, providing a platform for learning and sharing knowledge [3]. - Various technical areas related to autonomous driving are covered, including perception, mapping, and control, indicating a comprehensive approach to education in this field [3].