Pacman programming project

The goal of this programming project is to implement intelligent agents for the game of Pacman. The project is divided into three parts:

• Project 0: Implementation of a Search agent for eating all the food dots as quickly as possible.
• Project I: Implementation of a Minimax agent for eating all the food dots as quickly as possible, while avoiding the ghost enemies that are chasing you.
• Project II: Implementation of a Bayes filter for tracking all the non-visible ghosts' positions.

Table of contents

• Usage
• FAQ
  • Game score
  • API
  • Illegal moves
• Credits

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Usage

Start the game with a Pacman agent controlled by the keyboard (keys j, l, i, k or arrow keys):

python run.py

Options:

--agentfile: Start the game with a Pacman agent following a user-defined control policy:

python run.py  --agentfile randomagent.py

--ghostagent: Start the game with a ghost agent (either dumbyd, greedy or smarty):

python run.py  --ghostagent=greedy

--silentdisplay: Disable the graphical user interface:

python run.py --silentdisplay

--layout: Start the game with a user-specifed layout for the maze (see the /pacman_module/layouts/ folder):

python run.py --layout medium

--ghostagent: Start the game with a user-specifed ghost agent (see project I):

python run.py --ghostagent greedy

-h: For further details, check the command-line help section:

python run.py -h

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FAQ

Game score

The score function of the game is computed as follows:

score = -#time steps + 10*#number of eaten food dots - 5*#number of eaten capsules + 200*#number of eaten ghost + (-500 if #losing end) + (500 if #winning end).

We have implemented agents that win the game while maximizing their score.

API

Useful methods of the state are specified below:

• s.generatePacmanSuccessors() : Returns a list of pairs of successor states and moves given the current state s for the pacman agent.
  • This method must be called for any node expansion for pacman agent.
• s.generateGhostSuccessors(agentIndex) : Returns a list of pairs of successor states and moves given the current state s for the ghost agent indexed by agentIndex>0.
  • This method must be called for any node expansion for ghost agent.
• s.getLegalActions(agentIndex) : Returns a list of legal moves given the state s and the agent indexed by agentIndex. 0 is always the Pacman agent.
• s.getPacmanPosition() : Returns the Pacman position in a (x,y) pair.
• s.getScore() : Returns the total score of a state (as defined above).
• s.getFood() : Returns a boolean matrix which gives the position of all food dots.
• s.getNumFood() : Returns a scalar which gives the number of food dots remaining.
• s.getWalls() : Returns a boolean matrix which gives the position of all walls.
• s.getGhostPosition(agentIndex) : Returns the position of the ghost agent indexed by agentIndex>0.
• s.getGhostDirection(agentIndex) : Returns the direction of the ghost agent indexed by agentIndex>0.
• s.getCapsules() : Returns a list of positions of the remaining capsules in the maze.
• s.isWin() : Returns True if the state is in a winning end.
• s.isLose() : Returns True if the state is in a losing end.

Implementation examples are provided in humanagent.py and randomagent.py.

Illegal moves

The agent always returns a legal move. If it is not the case, the previous move is repeated if it is still legal. Otherwise, it remains in the same location.

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Credits

The programming projects are adapted from CS188 (UC Berkeley).

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Authors

• Andrea Gómez Herrera - andrea-liliana
• Thomas Rives - ThomasRives