Project 2: Multi-Agent Pac-Man

CSE 473 Project 2: Multi-Agent Pac-Man

CSE 473 Project 2: Multi-Agent Pac-Man
(100 points)

I'm gonna fake it to the left, and move to the right;
'Cause Pokey's too slow, and Blinky's out of sight.
I've got Pac-Man fever; It's driving me crazy.
I've got Pac-Man fever; I'm going out of my mind.
(from Pac-Man Fever by Buckner and Garcia)
Introduction
In this project, you will design agents for the classic version of Pac-Man, including ghosts. Along the
way, you will implement both minimax and expectimax search and try your hand at evaluation
function design.
The code base has not changed much from the previous project, but please start with a fresh
installation, rather than intermingling files from project 1. You can, however, use your search.py
and searchAgents.py in any way you want.
The code for this project contains the following files, available as a zip archive.
Key files to read
multiAgents.py Where all of your multi-agent search agents will reside.
pacman.py The main file that runs Pac-Man games. This file also describes a Pac-Man
GameState type, which you will use extensively in this project
game.py The logic behind how the Pac-Man world works. This file describes several
supporting types like AgentState, Agent, Direction, and Grid.
util.py Useful data structures for implementing search algorithms.
Files you can ignore
graphicsDisplay.py Graphics for Pac-Man
graphicsUtils.py Support for Pac-Man graphics
CSE 473 Project 2: Multi-Agent Pac-Man
https://courses.cs.washington.edu/courses/cse473/13au/pacman/multiagent/multiagentProject.html[10/16/2013 4:29:29 PM]
textDisplay.py ASCII graphics for Pac-Man
ghostAgents.py Agents to control ghosts
keyboardAgents.py Keyboard interfaces to control Pac-Man
layout.py Code for reading layout files and storing their contents
What to submit: You will fill in portions of multiAgents.py during the assignment. You should
submit this file containing your code and comments to the CSE 473 Dropbox. You may also submit
supporting files (like search.py, etc.) that you use in your code. Please do not change the other
files in this distribution or submit any of our original files other than multiAgents.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.
Getting Help: You are not alone! Feel free to use the class discussion board to discuss or get
clarifications on homework-related issues. If you find yourself stuck on something, go to office
hours or email the course staff for help. If you can't make our office hours, let us know and we will
schedule more. We want these projects to be rewarding and instructional, not frustrating and
demoralizing. But, we don't know when or how to help unless you ask. One more piece of advice: if
you don't know what a variable does or what kind of values it takes, print it out to see how it
behaves.
Multi-Agent Pac-Man
First, play a game of classic Pac-Man, preferably while listening to Pac-Man Fever:
python pacman.py
Now, run the provided ReflexAgent in multiAgents.py:
python pacman.py -p ReflexAgent
Note that it plays quite poorly even on simple layouts:
python pacman.py -p ReflexAgent -l testClassic
Inspect its code (in multiAgents.py) and make sure you understand what it's doing.
Question 1 (15 points) Improve the ReflexAgent in multiAgents.py to play respectably. The
provided reflex agent code provides some helpful examples of methods that query the GameState
for information. A capable reflex agent will have to consider both food locations and ghost locations
to perform well. Your agent should easily and reliably clear the testClassic layout:
python pacman.py -p ReflexAgent -l testClassic
Try out your reflex agent on the default mediumClassic layout with one ghost or two (and
animation off to speed up the display):
python pacman.py --frameTime 0 -p ReflexAgent -k 1
python pacman.py --frameTime 0 -p ReflexAgent -k 2
CSE 473 Project 2: Multi-Agent Pac-Man
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How does your agent fare? It will likely often die with 2 ghosts on the default board, unless your
evaluation function is quite good.
Note: you can never have more ghosts than the layout permits.
Note: As features, try the reciprocal of important values (such as distance to food) rather than just
the values themselves.
Note: The evaluation function you're writing is evaluating state-action pairs; in later parts of the
project, you'll be evaluating states.
Options: Default ghosts are random; you can also play for fun with slightly smarter directional
ghosts using -g DirectionalGhost. If the randomness is preventing you from telling whether your
agent is improving, you can use -f to run with a fixed random seed (same random choices every
game). You can also play multiple games in a row with -n. Turn off graphics with -q to run lots of
games quickly.
The autograder will check that your agent can rapidly clear the openClassic layout ten times
without dying more than twice or thrashing around infinitely (i.e. repeatedly moving back and forth
between two positions, making no progress).
python pacman.py -p ReflexAgent -l openClassic -n 10 -q
Don't spend too much time on this question, though, as the meat of the project lies ahead.
Question 2 (25 points) Now you will write an adversarial search agent in the provided
MinimaxAgent class stub in multiAgents.py. Your minimax agent should work with any number of
ghosts, so you'll have to write an algorithm that is slightly more general than what appears in the
textbook. In particular, your minimax tree will have multiple min layers (one for each ghost) for
every max layer.
Your code should also expand the game tree to a fixed depth, which will be specified at the
command line. Score the leaves of your minimax tree with the supplied
self.evaluationFunction, which defaults to scoreEvaluationFunction. MinimaxAgent extends
MultiAgentAgent, which gives access to self.depth and self.evaluationFunction. Make sure
your minimax code makes reference to these two variables where appropriate as these variables
are populated in response to command line options.
Important: A single search ply is considered to be one Pac-Man move and all the ghosts' responses,
so depth 2 search will involve Pac-Man and each ghost moving two times.
Hints and Observations
The evaluation function in this part is already written (self.evaluationFunction). You
shouldn't change this function, but recognize that now we're evaluating *states* rather than
actions, as we were for the reflex agent. Look-ahead agents evaluate future states whereas
reflex agents evaluate actions from the current state.
The minimax values of the initial state in the minimaxClassic layout are 9, 8, 7, -492 for
depths 1, 2, 3 and 4 respectively. Note that your minimax agent will often win (665/1000
games for us) despite the dire prediction of depth 4 minimax.
python pacman.py -p MinimaxAgent -l minimaxClassic -a
depth=4
To increase the search depth achievable by your agent, remove the Directions.STOP action
from Pac-Man's list of possible actions. Depth 2 should be pretty quick, but depth 3 or 4 will be
slow. Don't worry, the next question will speed up the search somewhat.
Pac-Man is always agent 0, and the agents move in order of increasing agent index.
All states in minimax should be GameStates, either passed in to getAction or generated via
GameState.generateSuccessor. In this project, you will not be abstracting to simplified
states.
On larger boards such as openClassic and mediumClassic (the default), you'll find Pac-Man to
be good at not dying, but quite bad at winning. He'll often thrash around without making
progress. He might even thrash around right next to a dot without eating it because he doesn't
know where he'd go after eating that dot. Don't worry if you see this behavior, question 5 will
clean up all of these issues.
CSE 473 Project 2: Multi-Agent Pac-Man
https://courses.cs.washington.edu/courses/cse473/13au/pacman/multiagent/multiagentProject.html[10/16/2013 4:29:29 PM]
When Pac-Man believes that his death is unavoidable, he will try to end the game as soon as
possible because of the constant penalty for living. Sometimes, this is the wrong thing to do
with random ghosts, but minimax agents always assume the worst:
python pacman.py -p MinimaxAgent -l trappedClassic -a
depth=3
Make sure you understand why Pac-Man rushes the closest ghost in this case.
Question 3 (20 points) Make a new agent that uses alpha-beta pruning to more efficiently
explore the minimax tree, in AlphaBetaAgent. Again, your algorithm will be slightly more general
than the pseudo-code in the textbook, so part of the challenge is to extend the alpha-beta pruning
logic appropriately to multiple minimizer agents.
You should see a speed-up (perhaps depth 3 alpha-beta will run as fast as depth 2 minimax).
Ideally, depth 3 on smallClassic should run in just a few seconds per move or faster.
python pacman.py -p AlphaBetaAgent -a depth=3 -l smallClassic
The AlphaBetaAgent minimax values should be identical to the MinimaxAgent minimax values,
although the actions it selects can vary because of different tie-breaking behavior. Again, the
minimax values of the initial state in the minimaxClassic layout are 9, 8, 7 and -492 for depths 1,
2, 3 and 4 respectively.
Question 4 (20 points) Random ghosts are of course not optimal minimax agents, and so
modeling them with minimax search may not be appropriate. Fill in ExpectimaxAgent, where your
agent will no longer take the min over all ghost actions, but the expectation according to your
agent's model of how the ghosts act. To simplify your code, assume you will only be running
against RandomGhost ghosts, which choose amongst their getLegalActions uniformly at random.
You should now observe a more cavalier approach in close quarters with ghosts. In particular, if
Pac-Man perceives that he could be trapped but might escape to grab a few more pieces of food,
he'll at least try. Investigate the results of these two scenarios:
python pacman.py -p AlphaBetaAgent -l trappedClassic -a
depth=3 -q -n 10
python pacman.py -p ExpectimaxAgent -l trappedClassic -a
depth=3 -q -n 10
You should find that your ExpectimaxAgent wins about half the time, while your AlphaBetaAgent
always loses. Make sure you understand why the behavior here differs from the minimax case.
Question 5 (20 points) Write a better evaluation function for pacman in the provided function
betterEvaluationFunction. The evaluation function should evaluate states, rather than actions
like your reflex agent evaluation function did. You may use any tools at your disposal for
evaluation, including your search code from the last project. With depth 2 search, your evaluation
function should clear the smallClassic layout with two random ghosts more than half the time
and still run at a reasonable rate (to get full credit, Pac-Man should be averaging around 1000
points when he's winning).
python pacman.py -l smallClassic -p ExpectimaxAgent -a
evalFn=better -q -n 10
Document your evaluation function! We're very curious about what great ideas you have, so don't
be shy. We reserve the right to reward bonus points for clever solutions and show demonstrations
in class.
Hints and Observations
As for your reflex agent evaluation function, you may want to use the reciprocal of important
values (such as distance to food) rather than the values themselves.
One way you might want to write your evaluation function is to use a linear combination of
features. That is, compute values for features about the state that you think are important,
CSE 473 Project 2: Multi-Agent Pac-Man
https://courses.cs.washington.edu/courses/cse473/13au/pacman/multiagent/multiagentProject.html[10/16/2013 4:29:29 PM]
and then combine those features by multiplying them by different values and adding the
results together. You might decide what to multiply each feature by based on how important
you think it is.
Mini Contest (15 points extra credit) Pac-Man's been doing well so far, but things are about to
get a bit more challenging. This time, we'll pit Pac-Man against smarter foes in a trickier maze. In
particular, the ghosts will actively chase Pac-Man instead of wandering around randomly, and the
maze features more twists and dead-ends, but also extra pellets to give Pac-Man a fighting chance.
You're free to have Pac-Man use any search procedure, search depth, and evaluation function you
like. The only limit is that games can last a maximum of 3 minutes (with graphics off), so be sure
to use your computation wisely. We'll run the contest with the following command:
python pacman.py -l contestClassic -p ContestAgent -g
DirectionalGhost -q -n 10
The three students with the highest score (details: we run 10 games; games longer than 3 minutes
get score 0; lowest and highest 2 scores discarded; the remaining 6 scores averaged) will receive
up to 15 extra-credit points depending on performance. Be sure to document what your agent is
doing, as we may award additional extra credit to creative solutions even if they're not in the top 3.
We may also demonstrate the performance of winning or creative solutions in class.
Congrats, you are done with Project 2!