标题： 基于视觉的赛车模拟环境驾驶代理 显示英文标题

标题： Vision based driving agent for race car simulation environments

摘要： 近年来，自动驾驶已成为一个热门的研究领域。由于在紧急情况下轮胎抓地力极限的控制至关重要，因此为赛车开发的算法也适用于道路车辆。本文研究了使用深度强化学习（DRL）解决模拟环境中轮胎抓地力极限驾驶问题的方法。采用近端策略优化（PPO）方法训练智能体，仅通过视觉输入来控制车辆的方向盘和踏板，以实现专业车手的圈速。论文概述了将赛道上时间最优驾驶任务表述为深度强化学习问题的过程，并解释了所选择的观察值、动作和奖励函数。结果展示了类似于人类的学习和驾驶行为，能够充分利用最大轮胎抓地潜力。 显示英文摘要

摘要： In recent years, autonomous driving has become a popular field of study. As control at tire grip limit is essential during emergency situations, algorithms developed for racecars are useful for road cars too. This paper examines the use of Deep Reinforcement Learning (DRL) to solve the problem of grip limit driving in a simulated environment. Proximal Policy Optimization (PPO) method is used to train an agent to control the steering wheel and pedals of the vehicle, using only visual inputs to achieve professional human lap times. The paper outlines the formulation of the task of time optimal driving on a race track as a deep reinforcement learning problem, and explains the chosen observations, actions, and reward functions. The results demonstrate human-like learning and driving behavior that utilize maximum tire grip potential.