This is a reinforcement learning agent that plays Ms. PacMan through function approximation.
This is a submission to the third assignment of McGill University's ECSE 526 - Artificial Intelligence course. Details can be found here.
This agent has been able to score an average of over 4000 points each episode, with a maximum recorded score of approximately 12500.
To run this, all one needs is Python 2.7 or above, the
Arcade Learning Environment with its Python bindings installed and
opencv-python.
If you're running this on OSX, you will also need pygame in order to display
the game screen.
To run, simply run:
python play.pyor
python play.py --helpfor advanced options.
When running, you should see a window as pictured above (titled ALE Viz). The
map and sliced map windows would appear when running with the --map-display
option. The map window shows the reduced approximation of the game field,
whereas the sliced map window is the portion of the map the agent is currently
analyzing. A legend of the colors used in the maps is shown below:
| Color | Meaning |
|---|---|
| Blue | Clear path |
| Orange | Wall |
| White | Pellet |
| Cyan | Power-up |
| Magenta | Fruit |
| Red | Bad ghost |
| Green | Edible ghost |
| Yellow | Ms. PacMan |