Contest 1: Multi-Agent Pacman (due Tuesday, September-22, 11:59pm)

Table of Contents

• Overview
• Extra Credit
• Quick Start Guide
• Introduction
• Rules
• Designing Agents
• Restrictions
• Getting Started
• Submission

Overview

In this contest, you will apply the search algorithms and problems implemented in Project 1 to handle more difficult scenarios that include controlling multiple pacman agents and planning under time constraints. There is room to bring your own unique ideas, and there is no single set solution. Much looking forward to seeing what you come up with!

Grading

Grading is assigned according to the following key:

• +1 points per staff bot (3 in total) beaten in the final ranking
• +0.1 points per student bot beaten in the final ranking
• 1st place: +2 points
• 2nd and 3rd place: +1.5 points

Quick Start Guide

1. Download the code (contest1.zip), unzip it, and change to the directory.
2. Copy your search.py from Project 1 into the minicontest directory (replacing the blank search.py).
3. Go into myAgents.py and fill out findPathToClosestDot in ClosestDotAgent, and isGoalState in AnyFoodSearchProblem. You should be able to copy your solutions from Project 1 over.
4. Run python pacman.py, and you should be able to see 4 pacman agents travelling around the map collecting dots
5. Submit the myAgents.py file to Contest 1 on eCampus.

Important! You only need to submit myAgents.py. If you import from search.py or searchProblems.py, the autograder will use the staff version of those files.

Introduction

The base code is nearly identical to Project 1, but with some minor modifications to include support for more than one Pacman agent. You can download all the code and supporting files as a zip archive. Some key differences:

• You will control N Pacman agents on the board at a time. Your code must be able to support a multiple number of agents
• There is a cost associate with how long Pacman "thinks" (compute cost). See Scoring for more details.

Files to Edit and Submit: You will fill and submit myAgents.py

Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation -- not the autograder's judgements -- will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.

Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don't try. We trust you all to submit your own work only; please don't let us down. If you do, we will pursue the strongest consequences available to us.

Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, section, and the discussion forum are there for your support; please use them. If you can't make our office hours, let us know and we will schedule more. We want these contests to be rewarding and instructional, not frustrating and demoralizing. But, we don't know when or how to help unless you ask.

Discussion: Please be careful not to post spoilers.

Rules

Layout

There are a variety of layout in the layouts directory. Agents will be exposed to a variety of maps of different sizes and amounts of food.

Scoring

The scoring from Project 1 is maintained, with a few modifications

Kept from Project 1

• +10 for each food pellet eaten
• +500 for collecting all food pellets

Modifications

• -0.4 for each action taken (Project 1 penalized -1)
• -1 * total compute used to calculate next action (in seconds) * 1000

Each agent also starts with 100 points.

Observations

Each agent can see the entire state of the game, such as food pellet locations, all pacman locations, etc. See the GameState section for more details.

Winning and Losing

Win: You win if you collect all food pellets. Your score is the current amount of points.

Lose: You lose if your score reaches zero. This can be caused by not finding pellets quickly enough, or spending too much time on compute. Your score for this game is zero. If your agent crashes, it automatically receives a score of zero.

Designing Agents

File Format

You should include your agents in a file of the same format as myAgents.py. Your agents must be completely contained in this one file. Though, you may use the functions in search.py

Interface

The GameState in pacman.py should look familiar, but contains some modifications to support multiple Pacman agents. The major change to note is that many GameState methods now have an extra argument, agentIndex, which is to identify which Pacman agent it needs. For example, state.getPacmanPosition(0) will get the position of the first pacman agent. For more information, see the GameState class in pacman.py.

Agent

To get started designing your own agent, we recommend subclassing the Agent class in game.py (this has already been done by default). This provides to one important variable, self.index, which is the agentIndex of the current agent. For example, if we have 2 agents, each agent will be created with a unique index, [MyAgent(index=0), MyAgent(index=1)], that can be used when deciding on actions.

The autograder will call the createAgents function to create your team of pacman. By default, it is set to create N identical pacmen, but you may modify the code to return a diverse team of multiple types of agents

python pacman.py

python pacman.py --help

python pacman.py --pacman myAgents.py

python pacman.py --layout test1.lay

python pacman.py --l test1.lay

python autograder.py --pacman myAgents.py