Welcome to Flow

Flow is a computational framework for deep RL and control experiments for traffic microsimulation. Visit our website for more information.

Flow is a work in progress - input is welcome. Available documentation is limited for now. Tutorials are available in iPython notebook format.

If you are looking for Akvo Flow, their documentation can be found at http://flowsupport.akvo.org.

Contents:

• Introduction
• Local Installation of Flow
• Virtual installation of Flow (using docker containers)
• Tutorials
• Creating Multiagent Environments
• Flow Renderer
• Visualization
• Regression Testing in Flow
• Built-in Traffic Networks
• Code Documentation

If you use Flow for academic research, you are highly encouraged to cite our paper:

C. Wu, A. Kreidieh, K. Parvate, E. Vinitsky, A. Bayen, “Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control,” CoRR, vol. abs/1710.05465, 2017. [Online]. Available: https://arxiv.org/abs/1710.05465

If you use the benchmarks, you are highly encouraged to cite our paper:

Vinitsky, E., Kreidieh, A., Le Flem, L., Kheterpal, N., Jang, K., Wu, F., … & Bayen, A. M. (2018, October). Benchmarks for reinforcement learning in mixed-autonomy traffic. In Conference on Robot Learning (pp. 399-409).

Indices and tables

• Index

• Module Index

• Search Page