Flow: a deep reinforcement learning framework for mixed-autonomy traffic
Flow leverages state-of-the-art deep RL libraries and the open-source microsimulator, SUMO, enabling the use of reinforcement learning to design and train controllers in traffic settings.
Flow was developed at the University of California, Berkeley.
Results
Successful controllers developed with Flow. For more details check out our gallery.
Phantom Shockwave Dissipation on a Ring
Intersection control
Bottleneck control
Inspired by the rapid decrease in lanes on the San Francisco-Oakland Bay Bridge, we study a bottleneck that merges from four lanes down to two to one.
We demonstrate that the AVs are able to learn a strategy that increases the effective outflow at high inflows, and performs competitively with ramp metering.
