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Flow: Deep Reinforcement Learning for Control in SUMO

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Figure

Figure 1: Networks supported in Flow. Clockwise from top left: merge network with two loops merging;single-lane ring; intersection grid network; close-up of intersections in grid; roads in downtown San Francisco,imported from OpenStreetMap; figure-eight scenario
Figure 2: Flow provides components to programatically generate networks and configuration, as well as aninterface with reinforcement learning libraries
Figure 3: Flow Process Diagram. Flow manages coordinating SUMO with the observations, actions, and rewardsneeded and produced by the reinforcement learning libraries by a reinforcement learning library
6.1Figure 4Case Study: Ring Roads
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