Ms. PacMan Agent

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.

Setup

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.

Running

To run, simply run:

python play.py

or

python play.py --help

for 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