以下文章来源于数据结构和算法 ，作者博哥 数据结构和算法 . 《算法秘籍》作者王一博，专注于互联网大厂热点事件和算法题讲解。 最近一网友收到一个offer，因为自己在洗澡没有看到，结果过了40分钟hr又把offer给撤回了， 关键hr还把他的联系方式给删了，也没办法争取了。 我对这种仅过了40分钟就撤回offer的行为很是不能理解，说明他们也不是真的很缺人，如果真的 缺人，也不会在乎那几十分钟，所以不去也挺好。 输入 ：arr = [1,-2,0,3] 输出 ：4 解释 ：我们可以选出 [1, -2, 0, 3]，然后删掉 -2，这样得到 [1, 0, 3]，和最大。 --------------下面是今天的算法题-------------- 来看下今天的算法题，这题是LeetCode的第 1186题：删除一次得到子数组最大和，难度是中等。 给你一个整数数组，返回它的某个非空子数组（连续元素）在执行一次可选的删除操作后，所能得 到的最大元素总和。换句话说，你可以从原数组中选出一个子数组，并可以决定要不要从中删除一 个元素（只能删一次哦），（删除后）子数组中至少应当有一个元素，然后该子数组（剩下）的元 素总和是 ...

Group 1 - The FormulaOne benchmark test reveals the limitations of top AI models, with GPT-5 achieving only about 4% accuracy on advanced questions and scoring zero on the most difficult problems [1][6][19] - The benchmark, developed by AAI, aims to measure algorithmic reasoning depth beyond competitive programming, focusing on real-world optimization problems [8][15] - The test consists of 220 novel graph-based dynamic programming problems categorized into three levels of difficulty: shallow, deeper, and deepest [16][18] Group 2 - AAI was founded by Amnon Shashua, co-founder of Mobileye, and focuses on AI research and development [10][11] - The benchmark's problems are designed to be easily understandable but require significant creativity and deep reasoning to solve [19][22] - The challenges presented in the deepest level of the benchmark highlight the gap between current AI capabilities and the reasoning required for complex real-world problems [25][30]

Core Viewpoint - The article discusses the advancements and potential of reinforcement learning (RL) in the field of autonomous driving, highlighting its evolution and comparison with other learning paradigms such as supervised learning and imitation learning [4][7][8]. Summary by Sections Background - The article notes the recent industry focus on new technological paradigms like VLA and reinforcement learning, emphasizing the growing interest in RL following significant milestones in AI, such as AlphaZero and ChatGPT [4]. Supervised Learning - In autonomous driving, perception tasks like object detection are framed as supervised learning tasks, where a model is trained to map inputs to outputs using labeled data [5]. Imitation Learning - Imitation learning involves training models to replicate actions based on observed behaviors, akin to how a child learns from adults. This is a primary learning objective in end-to-end autonomous driving [6]. Reinforcement Learning - Reinforcement learning differs from imitation learning by focusing on learning through interaction with the environment, using feedback from task outcomes to optimize the model. It is particularly relevant for sequential decision-making tasks in autonomous driving [7]. Inverse Reinforcement Learning - Inverse reinforcement learning addresses the challenge of defining reward functions in complex tasks by learning from user feedback to create a reward model, which can then guide the main model's training [8]. Basic Concepts of Reinforcement Learning - Key concepts include policies, rewards, and value functions, which are essential for understanding how RL operates in autonomous driving contexts [14][15][16]. Markov Decision Process - The article explains the Markov decision process as a framework for modeling sequential tasks, which is applicable to various autonomous driving scenarios [10]. Common Algorithms - Various algorithms are discussed, including dynamic programming, Monte Carlo methods, and temporal difference learning, which are foundational to reinforcement learning [26][30]. Policy Optimization - The article differentiates between on-policy and off-policy algorithms, highlighting their respective advantages and challenges in training stability and data utilization [27][28]. Advanced Reinforcement Learning Techniques - Techniques such as DQN, TRPO, and PPO are introduced, showcasing their roles in enhancing training stability and efficiency in reinforcement learning applications [41][55]. Application in Autonomous Driving - The article emphasizes the importance of reward design and closed-loop training in autonomous driving, where the vehicle's actions influence the environment, necessitating sophisticated modeling techniques [60][61]. Conclusion - The rapid development of reinforcement learning algorithms and their application in autonomous driving is underscored, encouraging practical engagement with the technology [62].