//Flow Publications

Where you can find relevant publications about the Flow project


Title, Author, Journal/Conference, Year File
"Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning", A. Kreidieh, C. Wu, A. Bayen, ITSC, 2018 Download
"Flow: Open Source Reinforcement Learning for Traffic Control", N. Kheterpal, E. Vinitsky, C. Wu, A. Kreidieh, K. Jang, K. Parvate, A. Bayen, Workshop on Machine Learning Open Source Software, NeurIPS, 2018 Download
"Benchmarks for reinforcement learning in mixed-autonomy traffic", E. Vinitsky, A. Kreidieh, L. Flem, N. Kheterpal, K. Jang, C. Wu, F. Wu, R. Liaw, E. Liang, A. Bayen, PMLR, Volume 87, 2018 Download
"Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion", E. Vinitsky, K. Parvate, A. Kreidieh, C. Wu, Z. Hu, A. Bayen, ITSC, 2018 Download
"Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control", C. Wu, A. Kreidieh, K. Parvate, E. Vinitsky, A. Bayen, arXiv preprint arXiv:1710.05465, 2017, When citing Flow, please cite this paper Download
"Emergent behaviors in mixed-autonomy traffic", C. Wu, A. Kreidieh, E. Vinitsky, A. Bayen, PMLR, Volume 78, 2017 Download
"Framework for Control and Deep Reinforcement Learning in Traffic", C. Wu, K. Parvate, N. Kheterpal, L. Dickstein, A. Mehta, E. Vinitsky, A. Bayen, ITSC, 2017 Download
"Flow: Deep Reinforcement for Control in SUMO", N. Kheterpal, K. Parvate, C. Wu, A. Kreidieh, E. Vinitsky, A. Bayen, SUMO User Conference, 2017 Download
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