模拟到缩放城市：通过自动驾驶车辆实现交通控制的零样本策略迁移
Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles
AI总结：
本文通过深度强化学习训练自动驾驶车辆在环形交叉口的控制策略，并将训练好的策略迁移至缩放智能城市进行测试，发现注入噪声的策略在迁移后表现更佳，实现了交通流的优化。
摘要：
使用深度强化学习，我们训练了自动驾驶车辆在车队中通过环形交叉口的控制策略。使用Flow库，我们在微仿真器中训练了两种策略：一种在状态和动作空间中注入噪声，另一种则没有。在模拟中，自动驾驶车辆为两种策略都学习出一种涌现的引导行为，即减速以实现更流畅的合并。随后，我们将该策略直接迁移至德雷克塞尔大学缩放智能城市（UDSSC）测试平台，该平台是连接和自动化车辆的1:25比例测试场。我们对两种策略在缩放城市中的性能进行了表征。结果显示，无噪声策略经常导致碰撞，仅偶尔实现引导；而注入噪声的策略则始终表现出引导行为且无碰撞，表明噪声有助于零样本策略迁移。此外，迁移后的噪声注入策略在UDSSC中使平均行程时间减少了5%，最大行程时间减少了22%。控制器的视频可在https://sites.google.com/view/iccps-policy-transfer查看。
英文摘要：
Using deep reinforcement learning, we train control policies for autonomous vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library for deep reinforcement learning in micro-simulators, we train two policies, one policy with noise injected into the state and action space and one without any injected noise. In simulation, the autonomous vehicle learns an emergent metering behavior for both policies in which it slows to allow for smoother merging. We then directly transfer this policy without any tuning to the University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles. We characterize the performance of both policies on the scaled city. We show that the noise-free policy winds up crashing and only occasionally metering. However, the noise-injected policy consistently performs the metering behavior and remains collision-free, suggesting that the noise helps with the zero-shot policy transfer. Additionally, the transferred, noise-injected policy leads to a 5% reduction of average travel time and a reduction of 22% in maximum travel time in the UDSSC. Videos of the controllers can be found at https://sites.google.com/view/iccps-policy-transfer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
