- Cameron Raymond - Computer Science, Queen's University
- Hugh Corley - Applied Mathematics, Queen's University
- Leonard Zhao - Biomedical Computing, Queen's University
- Nicolas Wlodek - Cognitive Computing, Queen's University
- Ross Hill - Software Design, Queen's University
- Zane Little - Cognitive Computing, Queen's University
Q-Learning for Traffic Signal Control
Performance Over Time
pip3 install -r requirements.txt
Config Values That You Can Customize
- Set the file that you'd like to save the Q-Table to in config.
- Set the Q-Table to load from (if you want to train from scratch you'll have the option to from command line).
- Set the number of years and day per years (default is 1 year of 10 days).
- Set the policy that you'd like to use (
- Set the environment dynamics in the config with
- Train/Plot -
- Visualize -
python3 Visualizer.py(this will also train but much slower due to as it has to render each new state)