Traffic systems can often be modeled by complex (nonlinear and coupled) dynamical systems for which classical analysis tools struggle to provide the understanding sought by transportation agencies, planners, and control engineers, mostly because of difficulty to provide analytical results on these. Deep reinforcement learning (RL) provides an opportunity to study complex traffic control problems involving interactions of humans, automated vehicles, and sensing infrastructure. The resulting control laws and emergent behaviors of the vehicles provide insight and understanding of the potential for automation of traffic through mixed fleets of autonomous and manned vehicles.
Flow is a traffic control benchmarking framework. Its provides a suite of traffic control scenarios (benchmarks), tools for designing custom traffic scenarios, and integration with deep reinforcement learning and traffic microsimulation libraries.