标题： LearningFlow：基于大语言模型的城市驾驶自动策略学习工作流 显示英文标题

标题： LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models

摘要： 最近在强化学习（RL）方面的进展展示了其在自动驾驶中的巨大潜力。 尽管有这种前景，但诸如手动设计奖励函数以及复杂环境中的低样本效率等挑战仍然阻碍了安全有效驾驶策略的发展。 为了解决这些问题，我们引入了LearningFlow，这是一个针对城市驾驶的创新自动化策略学习工作流。 该框架在整个RL训练过程中利用了多个大型语言模型（LLM）代理的合作。 LearningFlow包括一个课程序列生成过程和一个奖励生成过程，这两个过程协同工作，通过生成定制的训练课程和奖励函数来指导RL策略。 特别是，每个过程都由一个分析代理支持，该代理评估训练进度并为生成代理提供关键见解。 通过这些LLM代理的协作努力， LearningFlow在一系列复杂的驾驶任务中实现了策略学习的自动化，并显著减少了对人工奖励函数设计的依赖，同时提高了样本效率。 在高保真CARLA模拟器中进行了全面的实验，并与其他现有方法进行了比较，以证明我们提出的方法的有效性。 结果表明，LearningFlow在生成奖励和课程方面表现出色。 它在各种驾驶任务中也实现了优越的性能和强大的泛化能力，并对不同的RL算法表现出令人称赞的适应性。 显示英文摘要

摘要： Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To tackle these issues, we introduce LearningFlow, an innovative automated policy learning workflow tailored to urban driving. This framework leverages the collaboration of multiple large language model (LLM) agents throughout the RL training process. LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions. Particularly, each process is supported by an analysis agent that evaluates training progress and provides critical insights to the generation agent. Through the collaborative efforts of these LLM agents, LearningFlow automates policy learning across a series of complex driving tasks, and it significantly reduces the reliance on manual reward function design while enhancing sample efficiency. Comprehensive experiments are conducted in the high-fidelity CARLA simulator, along with comparisons with other existing methods, to demonstrate the efficacy of our proposed approach. The results demonstrate that LearningFlow excels in generating rewards and curricula. It also achieves superior performance and robust generalization across various driving tasks, as well as commendable adaptation to different RL algorithms.