用视觉输入训练赛车自动驾驶，达到人类车手最快圈速。
Vision based driving agent for race car simulation environments
- 基于视觉输入的强化学习控制，仅用摄像头数据决策
- 采用PPO算法实现接近人类车手的极限操控表现
- 适合研究自动驾驶与强化学习融合的开发者
近年来，自动驾驶成为研究热点。由于在紧急情况下轮胎抓地力极限控制至关重要，赛车控制算法对普通汽车也有参考价值。本文研究使用深度强化学习（DRL）解决模拟环境中的抓地力极限驾驶问题。采用近端策略优化（PPO）方法训练智能体，仅通过视觉输入控制方向盘和油门刹车踏板，以达成专业人类车手的单圈时间。论文将赛道最优圈速问题建模为深度强化学习任务，详细说明了观测、动作与奖励函数的设计。结果表明，该智能体展现出类人学习与驾驶行为，能有效利用轮胎的最大抓地潜力。
原文摘要 · Abstract (English)
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.
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