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-qto run without UI-lto specify the layout (the code was written for-l smallGridand-l mediumClassic)-nto specify how many times to run the game (e.g.:-n 25)
These constants are used to generate the utiliy values
| Constant | Default |
|---|---|
| EMPTY_LOCATION_REWARD | -0.04 |
| FOOD_REWARD | 10 |
| CAPSULE_REWARD | 100 |
| GHOST_REWARD | -1000 |
| GAMMA | 0.9 |
| DANGER_ZONE_RATIO | 6 |
| DANGER | 500 |
| ITERATIONS | 10 |