Flow: Open Source Reinforcement Learning for Traffic Control

Nishant Kheterpal, Eugene Vinitsky, Cathy Wu, Aboudy Kreidieh, Kathy Jang, Kanaad Parvate, Alexandre Bayen

Abstract: This work presents Flow, an open-source Python library enabling the application of distributed reinforcement learning (RL) to mixed-autonomy traffic control tasks, in which autonomous vehicles, human-driven vehicles, and infrastructure interact. Flow integrates SUMO, a traffic microsimulator, with RLlib, a distributed reinforcement learning library. Using Flow, researchers can programatically design new traffic networks, specify experiment configurations, and apply control to autonomous vehicles and intelligent infrastructure. We have used Flow to train autonomous vehicles to improve traffic flow in a wide variety of representative traffic scenarios; the results and scripts to generate the networks and perform training have been integrated into Flow as benchmarks. Community use of Flow is central to its design; extensibility and clarity were considered throughout development. Flow is available for open-source use at flow-project.github.io and github.com/flow-project/flow.

Decision: accept