Pacman

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

• Part 1. You must implement a Search agent for eating all the food dots as quickly as possible.
• Part 2. You must implement a Minimax agent for eating all the food dots as quickly as possible, while avoiding the ghost enemies that are chasing you.
• Part 3. You must implement a MDP agent for eating all the food dots as quickly as possible, in a maze where some of the walls may appear and disappear at random.

Table of contents

• Installation
  • Setup
  • Usage
• Instructions
  • Part 1: Search agent
  • Part 2: Minimax agent
  • Part 3: MDP agent
• FAQ
  • Game score
  • API
  • Illegal moves
• Evaluation
• Credits

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Installation

The instructions below have been tested under Windows, Linux and MacOS.

We recommend to install a Python (3) environment using the Anaconda distribution. Further instructions can be found on the Anaconda user guide. Once installed, open the Anaconda prompt (Windows) or a terminal (Linux/MacOS).

Setup

(Linux/MacOS) Create a pacman environment and activate it:

conda create -n pacman python=3.6
source activate pacman

(Windows) Create a pacman environment and activate it:

conda create -n pacman python=3.6
activate pacman

From now, it is assumed that pacman is activated.

Dependencies should be installed through conda:

conda install numpy

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

--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

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

python run.py -h

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Instructions

For each part of the project, you must provide the following deliverables:

• The source code of your Pacman agent(s).
• A report in PDF format of 4 pages (at most).

The three parts of the project must be carried out in groups of 2 students (with the same group across all parts).

You deliverables must be submitted as an archive on the Montefiore submission platform.

Part 1: Search agent

This part is due on October 26, 2018 at 23:59. This is a hard deadline.

In this first part of the project, only food dots are in the maze. No ghost is present. Your task is to design an intelligent based on search algorithms (see Lecture 2) for eating all the dots as quickly as possible.

You are asked to implement an agent based on each of these search algorithms:

• Depth-First Search (DFS)
• Breadth-First Search (BFS)
• Uniform Cost Search (UCS)
• A* (and an associated heuristic of your choice).

Each agent should be implemented as a PacmanAgent class. Each should be specified in a different Python file (dfs.py, bfs.py, ucs.py, astar.py), following the template of pacmanagent.py.

Your report should be organized into 3 parts:

1. You must formalize the game as a search problem, as seen in Lecture 2.
2. You should run your agents against the 3 maze layouts located the /pacman_module/layouts/ folder. For each layout, report as a bar plot the performance of your 4 agents in terms of i) final score, ii) total computation time and iii) total number of expanded nodes. In total, you should therefore produce 9 bar plots.
3. Discuss the performance and limitations of your agents, with respect its search algorithm, the heuristic (in the case of A*) and the maze layout. Comment on possible improvements.

Part 2: Minimax agent

TBD.

Part 3: MDP agent

TBD.

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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 + 200*#number of eaten ghost + (-500 if #losing end) + (500 if #winning end).

We ask you to implement an agent that wins the game while maximizing its function.

Note that you should ask yourself if this score function satisfies all the properties of the search algorithms you will implement. If not, you are free to modify it as long the optimal solutions remain the same.

API

You must implement your agent as a PacmanAgent class, following the template of pacmanagent.py. The core of your algorithm should be implemented or called within the get_action method. This method receives the current state s of the game and should return the action to take.

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.
• 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.getWalls() : Returns a boolean matrix which gives the position of all walls.
• s.getGhostPositions() : Returns the position of all ghosts in the maze.
• 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

You need to ensure that your 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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Evaluation

The evaluation of your deliverables is based on the following criteria:

• Performance of your intelligent agent (as evaluated by its final score, its total computation time and its total number of expanded nodes). Note that your agents might be evaluated on mazes that are not included within the /pacman_module/layouts/ folder.

• Quality and clarity of your report.

  • Avoid long and vague sentences and be straight to the point.
  • Follow the structure mentioned in the instructions.

• Quality, clarity and structure of the source code.

  • Avoid single-file long code source, and prefers to use a multiple-files modular architecture.
  • Name your variables-attributes-classes according to their usage.
  • Comment your code so that explanations are concise and clear enough to allow the reader to understand the semantics in a quick look.
  • Your source code must be Python 3 and PEP8 compatible.

⚠️ Plagiarism is checked and sanctioned by a grade of 0.

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Credits

Credits: UC Berkeley