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)
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
searchAgents.py in any way you want.
The code for this project contains the following files, available as a multiagent.zip.
||Where all of your multi-agent search agents will reside.|
|The main file that runs Pac-Man games. This file also describes a Pac-Man |
||The logic behind how the Pac-Man world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid.|
||Useful data structures for implementing search algorithms.|
||Graphics for Pac-Man|
||Support for Pac-Man graphics|
||ASCII graphics for Pac-Man|
||Agents to control ghosts|
||Keyboard interfaces to control Pac-Man|
||Code for reading layout files and storing their contents|
What to submit: You will fill in portions of
during the assignment. You should submit this file containing your code and comments. 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. You should submit these files along with a report. Your code should be well-documented. All the project submissions must be made through blackboard.
Report (5 points) This should include stats such as number of nodes expanded, memory usage and running time for each search strategy that you have used in this project. The report should conclude with a critical analysis of the search methods based on your collected stats.
Document (5 points) Your code will be reviewed for good documentation.
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.
Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours are there for your support; please use them. You are also welcome to contact us via email.
First, play a game of classic Pac-Man, preferably while listening to Pac-Man Fever:
python pacman.pyNow, run the provided
python pacman.py -p ReflexAgentNote that it plays quite poorly even on simple layouts:
python pacman.py -p ReflexAgent -l testClassicInspect its code (in
multiAgents.py) and make sure you understand what it's doing.
Question 1 (10 points) Improve the
multiAgents.py to play respectably. The provided reflex agent code provides some helpful examples of methods that query the
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
python pacman.py -p ReflexAgent -l testClassicTry out your reflex agent on the default
mediumClassiclayout 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 2How 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 (40 points) Now you will write an adversarial search agent in the provided
MinimaxAgent class stub in
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 fix depth, which
will be specified at the command line. Score the leaves of your minimax
tree with the supplied
self.evaluationFunction, which defaults to
MultiAgentAgent, which gives access to
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
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.
minimaxClassiclayout 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
Directions.STOPaction 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.
GameStates, either passed in to
getActionor generated via
GameState.generateSuccessor. In this project, you will not be abstracting to simplified states.
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.
python pacman.py -p MinimaxAgent -l trappedClassic -a depth=3Make sure you understand why Pac-Man rushes the closest ghost in this case.
Question 3 (40 points) Make a new agent that uses alpha-beta pruning to more efficiently explore the minimax tree, in
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
AlphaBetaAgent minimax values should be identical to the
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.