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Computational framework for reinforcement learning in traffic control

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Flow

Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

See our website for more information on the application of Flow to several mixed-autonomy traffic scenarios. Other results and videos are available as well.

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Technical questions

Please direct your techincal questions to Stack Overflow using the flow-project tag.

Getting involved

We welcome your contributions.

Citing Flow

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).

Contributors

Flow is supported by the Mobile Sensing Lab at UC Berkeley and Amazon AWS Machine Learning research grants. The contributors are listed in Flow Team Page.

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