对比闭环训练方法，找出更真实的自动驾驶交通代理建模方案。
Analyzing Closed-loop Training Techniques for Realistic Traffic Agent Models in Autonomous Highway Driving Simulations
- 对比开环与闭环多智能体训练差异
- 发现对抗性监督比确定性方法更有效
- 适合研究自动驾驶仿真与真实场景对接的学者
仿真在自动驾驶车辆的快速开发与安全部署中起着关键作用。真实的交通代理模型对于弥合仿真与现实世界之间的差距至关重要。现有模仿人类行为的方法大多基于从示范中学习，但常受限于单一训练策略。为促进对真实交通代理建模的全面理解，本文对不同训练原则进行了广泛比较，重点关注高速路驾驶仿真中的闭环方法。我们实验对比了（i）开环与闭环多智能体训练、（ii）对抗性与确定性监督训练、（iii）强化学习损失的影响，以及（iv）与日志重放代理共同训练的效果，以识别适用于真实代理建模的训练技术。此外，我们还发现了几种有效的闭环训练方法组合。
原文摘要 · Abstract (English)
Simulation plays a crucial role in the rapid development and safe deployment of autonomous vehicles. Realistic traffic agent models are indispensable for bridging the gap between simulation and the real world. Many existing approaches for imitating human behavior are based on learning from demonstration. However, these approaches are often constrained by focusing on individual training strategies. Therefore, to foster a broader understanding of realistic traffic agent modeling, in this paper, we provide an extensive comparative analysis of different training principles, with a focus on closed-loop methods for highway driving simulation. We experimentally compare (i) open-loop vs. closed-loop multi-agent training, (ii) adversarial vs. deterministic supervised training, (iii) the impact of reinforcement losses, and (iv) the impact of training alongside log-replayed agents to identify suitable training techniques for realistic agent modeling. Furthermore, we identify promising combinations of different closed-loop training methods.
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