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.
intro flow_setup flow_examples tutorials multiagent rendering visualizing regression examples modules
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).
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