Reinforcement Learning (RL) has emerged as a transformative approach in the domains of automation and robotics, offering powerful solutions to complex problems that conventional methods struggle to address. In scenarios where the problem definitions are elusive and challenging to quantify, learning-based solutions such as RL become particularly valuable. One instance of such complexity can be found in the realm of car racing, a dynamic and unpredictable environment that demands sophisticated decision-making algorithms. This study focuses on developing and training an RL agent to navigate a racing environment solely using feedforward raw lidar and velocity data in a simulated context. The agent's performance, trained in the simulation environment, is then experimentally evaluated in a real-world racing scenario. This exploration underlines the feasibility and potential benefits of RL algorithm enhancing autonomous racing performance, especially in the environments where prior map information is not available.

翻译：强化学习（RL）已成为自动化和机器人领域的一种变革性方法，为传统方法难以应对的复杂问题提供了强大的解决方案。在问题定义模糊且难以量化的场景中，基于学习的方法（如强化学习）尤为有价值。汽车竞赛正是这类复杂性的一个典型实例——这是一个动态且不可预测的环境，需要复杂的决策算法。本研究聚焦于开发并训练一个强化学习智能体，使其仅利用前馈原始激光雷达数据和速度信息，在仿真环境中完成赛道导航。该智能体在仿真环境中完成训练后，其性能通过真实赛车场景的实验进行评估。本探索突显了强化学习算法在提升自动驾驶赛车性能方面的可行性与潜在优势，特别是在缺乏先验地图信息的环境中。