This work shows the implementation and statistical analysis of an AI agent capable of winning the arcade game of Pac-Man using an MDP solver that follows a policy based on Value Iteration.

A full report can be found here.

The game itself is also modelled as a stochastic variation of the Pac-Man game, meaning that some transitions are probabilistic. In the context of the Pac-Man game, the agent has an 80% probability of going in the direction specified by the policy, and a 10% change of going to either direction perpendicular to that.If the agent hits a wall, it will not move.

The sole file here is meant to be used with Berkley's Pac-Man Projects . It therefore only contains the logic associated with a MDP agent trying to win the Pac-Man game.

The code is meant to be run on Python 2.7

Run the agent on a small grid:

python pacman.py -p MDPAgent -l smallGrid

• -q to run without UI
• -l to specify the layout (the code was written for -l smallGrid and -l mediumClassic)
• -n to specify how many times to run the game (e.g.: -n 25)

These constants are used to generate the utiliy values