reinforcement-learning-sumo
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mschrader15
Reinforcement Learning + traffic microsimulation (via SUMO). Uses Ray RLLIB and forces SUMO into the OpenAI Gym Framework
Reinforcement Learning + SUMO
Applying reinforcement learning to traffic microsimulation (SUMO)
A minimal example is available in the example folder
Very much a WIP
Structure
This project follows the structure of FLOW closely. I only chose to diverge from FLOW because it abstracted the XML creation for SUMO. For me, this repository plugs in to a greater code-base, that turns real-world ITS data into SUMO traffic demand and traffic light operation. Those tools work ultimately by writing to SUMO XML files, and we didn't want to convert to the FLOW pythonic framework.
Results
Full breakdown: https://maxschrader.io/reinforcement-learning-and-sumo
video on Youtube
ES vs. PPO
RL-Controlled Traffic Signals vs. Calibrated Real-World Simulation
Real World Traffic Signals during Simulation Period
RL-Controlled Traffic Signals during Simulation Period
Metadata
Owner
mschrader15
Metadata
Reinforcement Learning + traffic microsimulation (via SUMO). Uses Ray RLLIB and forces SUMO into the OpenAI Gym Framework