用强化学习训练自动驾驶测试司机，能像顶尖车手一样评估赛车调校。
Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
- 通过深度强化学习构建可自主驾驶的测试司机模型。
- 模型在仿真中表现媲美顶尖人类车手，可高效评估车辆调校效果。
- 融合模仿学习使模型行为更贴近真人，支持个性化调校优化。
赛车成功依赖于车辆调校、赛道理解与人类经验的结合。由于真实世界中构建和测试多种车辆配置成本过高，高保真仿真成为赛车开发的关键环节。然而，评估不同配置在赛道上的表现仍需依赖专家人工输入。本文首次提出自主测试司机框架，基于深度强化学习训练，能够在仿真中以顶尖人类车手水平评估车辆调校对竞速性能的影响。此外，通过在强化学习中引入模仿学习，模型可被调整为更接近人类行为模式，从而实现针对特定驾驶员的车辆配置优化。
原文摘要 · Abstract (English)
Success in racing requires a unique combination of vehicle setup, understanding of the racetrack, and human expertise. Since building and testing many different vehicle configurations in the real world is prohibitively expensive, high-fidelity simulation is a critical part of racecar development. However, testing different vehicle configurations still requires expert human input in order to evaluate their performance on different racetracks. In this work, we present the first steps towards an autonomous test driver, trained using deep reinforcement learning, capable of evaluating changes in vehicle setup on racing performance while driving at the level of the best human drivers. In addition, the autonomous driver model can be tuned to exhibit more human-like behavioral patterns by incorporating imitation learning into the RL training process. This extension permits the possibility of driver-specific vehicle setup optimization.
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