Master's thesis - BGSE Data Science
Authors: Monika Matyja, Sebastian Wolf and Jordi Morera
Supervisors: Hrvoje Stojic and Anestis Papanikolaou
We develop a traffic light control agent that can manage traffic lights with the objective to reduce traffic jams, trip time and other traffic metrics in a given network using reinforcement learning. To this end, we implement a Double Deep Q-Network algorithm and test its performance in controlling traffic lights on a ’small’ and a ’large’ traffic junction. We find that this algorithm beats a fixed traffic light phase program when traffic demand fluctuates, as it is capable of reacting to real-time traffic situations. The algorithm can be scaled up and holds promise to also perform well in controlling larger transport networks. For more information check the report.
- Scripts: Any script is needed to run the simulation. Also a couple of examples of how to run the code
- Report
- Videos: Recordings of an evaluation episode in split screen: DDQN agent vs benchmark policy
Option 1
Run setup.sh
bash setup.sh
Option 2
Install sumo binaries
sudo apt-get install sumo sumo-tools sumo-doc
Set SUMO_HOME variable (default sumo installation path is /usr/share/sumo)
echo 'export SUMO_HOME="/usr/share/sumo"' >> ~/.bashrc
source ~/.bashrc
Installing required packages
pip3 install numpy tensorflow keras
Option 3
Pull Docker image
docker pull sebxwolf/sumo
Run Docker container
docker run -d -p 5901:5901 -p 6901:6901 -p 8888:8888 sebxwolf/sumo
In your browser, open: http://localhost:6901/?password=vncpassword
In the Desktop that opens, go to Applications and open Terminal Emulator
In the Terminal Emulator that opens, run
jupyter notebook --NotebookApp.token=admin --ip 0.0.0.0 --allow-root
In your browser, open localhost:8888/tree
The password is 'admin' and you can run all your notebooks and tutorials
(based on lucasfischerberkeley/flowdesktop)
In terminal, run:
tensorboard --logdir ./Scripts/Tests_Sumo/cross_RL/Logs/[name of experiment]
Then open a browser and enter:
http://localhost:6006/