基于深度强化学习的轨迹规划

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].