在全球经济格局风云变幻、金融市场波动加剧的当下，一场汇聚期货行业顶尖智慧与实战经验的盛会 ——第十九届全国期货（期权）实盘交易大赛暨第十二届全球衍生品实盘交易大赛颁奖大会将于11月15 日在古城西安举行。消息一经发布，便吸引了来自全国各地的报名者，他们有着共同的期待：在这个精 英云集的平台上学习知识、拓展人脉、探寻破局之道。 "今年的市场波动较大且节奏变化较快，以往熟悉的交易模式似乎不再那么奏效，每一个决策都充满挑 战。"首次参加颁奖大会的交易者温晶坦言，希望在会上结识更多志同道合的同行者，也特别期待听到 专家对未来趋势的分析预测，还有那些能在剧烈波动中实现稳定盈利的交易策略分享。 "这两年钢材现货贸易不好做，今年在朋友的推荐下，我开始做期现业务。作为期货行业的新手，我更 希望通过这次颁奖大会与高手交流，向他们学习先进的风控理念和交易系统，弥补自身的不足。"钢贸 商吴女士告诉记者。 作为行业年度盛会，本次颁奖大会承载着多重使命，不仅有对优秀交易者的嘉奖，更是一个经验传承与 智慧碰撞的平台。 在实盘大赛金牌导师王志新看来，颁奖大会既是行业精英的荣耀殿堂，也是投资者突破认知边界、获取 实战智慧的高效平台。个人投资 ...

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].