Emerging vehicular systems with increasing proportions of automated
components present opportunities for optimal control to mitigate congestion and
increase efficiency. There has been a recent interest in applying deep
reinforcement learning (DRL) to these nonlinear dynamical systems for the
automatic design of effective control strategies. Despite conceptual advantages
of DRL being model-free, studies typically nonetheless rely on training setups
that are painstakingly specialized to specific vehicular systems. This is a key
challenge to efficient analysis of diverse vehicular and mobility systems. To
this end, this article contributes a streamlined methodology for vehicular
microsimulation and discovers high performance control strategies with minimal
manual design. A variable-agent, multi-task approach is presented for
optimization of vehicular Partially Observed Markov Decision Processes. The
methodology is experimentally validated on mixed autonomy traffic systems,
where fractions of vehicles are automated; empirical improvement, typically
15-60% over a human driving baseline, is observed in all configurations of six
diverse open or closed traffic systems. The study reveals numerous emergent
behaviors resembling wave mitigation, traffic signaling, and ramp metering.
Finally, the emergent behaviors are analyzed to produce interpretable control
strategies, which are validated against the learned control strategies.

本文提出了一种简化的车辆微观模拟方法，并使用深度强化学习优化了具有不同车辆组成的六个交通系统的控制策略，发现了类似于波浪消减、交通信号和匝道计量等的多种新行为，并分析了这些行为以获得可解释的控制策略。