This is an implementation of an AI agent that is able to play and win Pac-Man through the use of value iteration to solve the Markov Decision Process (MDP).
More interestingly, the AI agent operates in a stochastic environment, as its moves are influenced by probabilities. For each move, the agent has an 80% chance to move in the value-iterated policy direction, and a 20% chance to move in a direction perpendicular to the value-iterated direction.
Original Pac-Man project developed at UC Berkley.
This project must be run using Python 2.7.
To run the agent:
python pacman.py -p MDPAgent -l <layout>
The layouts for the agent's environment can be found in the layouts directory. However, the agent was primarily developed to run on the smallgrid and mediumclassic layouts.
Aside from the -l option to specify the environment's layout, there are a couple of additional options that can be specified:
-qruns the agent without the UI.-n <number_of_games>can be used to specify how many Pac-Man games will be executed, where<number_of_games>is an integer value.
The following runs the agent on the smallgrid layout for 25 games without the UI:
python pacman.py -p MDPAgent -l smallgrid -q -n 25