用视觉和几何特征直接控制赛车，实现安全高效自动驾驶。
Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning
- 结合视觉感知与规则控制器，不依赖端到端强化学习
- 训练时间短、可解释性强，比赛中大幅领先其他参赛者
- 适合对可靠性与可调性要求高的真实自动驾驶场景
本文介绍我们在AIcrowd举办的2022年自主赛车虚拟挑战赛中获胜方案的实验与设计思路。比赛目标是推动自动驾驶技术边界，尤其关注安全性提升。竞赛被定义为强化学习（RL）挑战，我们初期尝试基于软演员-评论家（SAC）的变体方法，旨在仅从视觉和几何特征中学习非平凡的车辆控制，直接将像素映射为控制动作。通过调整默认奖励策略以促进平滑转向与加速控制，我们构建了一个系统：在实时仿真环境中，单个学习周期以分钟计时。未采用多任务并行化，而是选择传统路径——利用学习得到的感知算子处理视觉输入，并输入规则控制器。尽管该系统在学术上不如端到端像素到动作模型吸引人，但其训练需求低、可解释性强、泛化能力好且易于调试，最终在比赛中显著优于所有其他参赛者。
原文摘要 · Abstract (English)
This work presents the experiments and solution outline for our teams winning submission in the Learn To Race Autonomous Racing Virtual Challenge 2022 hosted by AIcrowd. The objective of the Learn-to-Race competition is to push the boundary of autonomous technology, with a focus on achieving the safety benefits of autonomous driving. In the description the competition is framed as a reinforcement learning (RL) challenge. We focused our initial efforts on implementation of Soft Actor Critic (SAC) variants. Our goal was to learn non-trivial control of the race car exclusively from visual and geometric features, directly mapping pixels to control actions. We made suitable modifications to the default reward policy aiming to promote smooth steering and acceleration control. The framework for the competition provided real time simulation, meaning a single episode (learning experience) is measured in minutes. Instead of pursuing parallelisation of episodes we opted to explore a more traditional approach in which the visual perception was processed (via learned operators) and fed into rule-based controllers. Such a system, while not as academically "attractive" as a pixels-to-actions approach, results in a system that requires less training, is more explainable, generalises better and is easily tuned and ultimately out-performed all other agents in the competition by a large margin.
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