Pacman, now with ghosts.
Minimax, Expectimax,
Evaluation.
In this project, you will design agents for the classic version of Pacman, 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.
multiAgents.py |
Where all of your multi-agent search agents will reside. |
pacman.py
| The main file that runs Pacman games. This file also describes a Pacman GameState type, which you will use extensively in this project |
game.py |
The logic behind how the Pacman world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid. |
util.py |
Useful data structures for implementing search algorithms. |
graphicsDisplay.py |
Graphics for Pacman |
graphicsUtils.py |
Support for Pacman graphics |
textDisplay.py |
ASCII graphics for Pacman |
ghostAgents.py |
Agents to control ghosts |
keyboardAgents.py |
Keyboard interfaces to control Pacman |
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 with 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
.
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.
Piazza: Post your questions (but not project solutions) on the Piazza page for this course.
First, play a game of classic Pacman:
python pacman.pyNow, run the provided
ReflexAgent
in multiAgents.py
:
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 (3 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 testClassicTry 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 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 (5 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 an arbitrary depth. 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 Pacman move and all the ghosts' responses, so depth 2 search will involve Pacman 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.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
Directions.STOP
action from Pacman'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 getAction
or generated via GameState.generateSuccessor
. In this project, you will not be abstracting to simplified states.
openClassic
and mediumClassic
(the default), you'll find Pacman 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 Pacman rushes the closest ghost in this case.
Question 3 (3 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 (3 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
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
getLegalAction
s uniformly at random.
You should now observe a more cavalier approach in close quarters with ghosts. In particular, if Pacman 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 10You 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 (6 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, Pacman 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
Project 2 is done. Go Pacman!