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Assignment 2 Pacman Problem


Assignment 2
Pacman Problem
This part is adapted from UC Berkeley and UW.
Introduction
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.
As in project 1, this project includes an autograder for you to grade your answers on
your machine. This can be run on all questions with the command:
python autograder.py
Note: If your python refers to Python 2.7, you may need to invoke python3 autograder.py
(and similarly for all subsequent Python invocations) or create a conda environment as
described in Project 0.
It can be run for one particular question, such as q2, by:
python autograder.py -q q2
It can be run for one particular test by commands of the form:
python autograder.py -t test_cases/q2/0-small-tree
By default, the autograder displays graphics with the -t option, but doesn’t with the -q
option. You can force graphics by using the –graphics flag, or force no graphics by using
the –no-graphics flag.
See the autograder tutorial in Project 0 for more information about using the autograder.
You can download all the code and supporting files as a zip archive from Files/Assignment
2/multiagent.zip.
The code for this project contains the following files:
Files you’ll edit:
• multiAgents.py → Where all of your multi-agent search agents will reside.
Files you might want to look at:
• 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. You don’t
need to use these for this project, but may find other functions defined here to be
useful.
Supporting files you can ignore:
• 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
• autograder.py → Project autograder
• testParser.py → Parses autograder test and solution files
• testClasses.py → General autograding test classes
• test_cases/ Directory containing the test cases for each question
• searchTestClasses.py → Project 2 specific autograding test classes.
Welcome to Multi-Agent Pacman
First, play a game of classic Pacman by running the following command:
python pacman.py
and using the arrow keys to move. 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.
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Question 1 (4 points): Reflex Agent
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
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: 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.
Note: You may find it useful to view the internal contents of various objects for debugging. You can do this by printing the objects’ string representations. For example, you
can print newGhostStates with print(str(newGhostStates)).
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.
Grading: We will run your agent on the openClassic layout 10 times. You will receive 0
points if your agent times out, or never wins. You will receive 1 point if your agent wins
at least 5 times, or 2 points if your agent wins all 10 games. You will receive an addition
1 point if your agent’s average score is greater than 500, or 2 points if it is greater than
1000. You can try your agent out under these conditions with
python autograder.py -q q1
To run it without graphics, use:
python autograder.py -q q1 --no-graphics
Don’t spend too much time on this question, though, as the meat of the project lies
ahead.
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Question 2 (5 points): Minimax
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 you’ve previously
seen in lecture. 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 MultiAgentSearchAgent, 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.
Grading: We will be checking your code to determine whether it explores the correct
number of game states. This is the only reliable way to detect some very subtle bugs in
implementations of minimax. As a result, the autograder will be very picky about how
many times you call GameState.generateSuccessor. If you call it any more or less than
necessary, the autograder will complain. To test and debug your code, run
python autograder.py -q q2
This will show what your algorithm does on a number of small trees, as well as a pacman
game. To run it without graphics, use:
python autograder.py -q q2 --no-graphics
Hints and Observations
• The correct implementation of minimax will lead to Pacman losing the game in
some tests. This is not a problem: as it is correct behaviour, it will pass the tests.
• The evaluation function for the Pacman test in this part is already written (self.evaluationFunctiYou 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
• Pacman is always agent 0, and the agents move in order of increasing agent index.
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• 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
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.
• When Pacman 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 Pacman rushes the closest ghost in this case.
Question 3 (5 points): Alpha-Beta Pruning
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
pseudocode from lecture, 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.
Grading: Because we check your code to determine whether it explores the correct
number of states, it is important that you perform alpha-beta pruning without reordering
children. In other words, successor states should always be processed in the order returned by GameState.getLegalActions. Again, do not call GameState.generateSuccessor
more than necessary.
You must not prune on equality in order to match the set of states explored by our
autograder. (Indeed, alternatively, but incompatible with our autograder, would be to
also allow for pruning on equality and invoke alpha-beta once on each child of the root
node, but this will not match the autograder.)
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The pseudo-code below represents the algorithm you should implement for this question.
To test and debug your code, run
python autograder.py -q q3
This will show what your algorithm does on a number of small trees, as well as a pacman
game. To run it without graphics, use:
python autograder.py -q q3 --no-graphics
The correct implementation of alpha-beta pruning will lead to Pacman losing some of
the tests. This is not a problem: as it is correct behaviour, it will pass the tests.
Question 4 (5 points): Expectimax
Minimax and alpha-beta are great, but they both assume that you are playing against an
adversary who makes optimal decisions. As anyone who has ever won tic-tac-toe can tell
you, this is not always the case. In this question you will implement the ExpectimaxAgent,
which is useful for modeling probabilistic behavior of agents who may make suboptimal
choices.
As with the search and constraint satisfaction problems covered so far in this class, the
beauty of these algorithms is their general applicability. To expedite your own development, we’ve supplied some test cases based on generic trees. You can debug your
implementation on small the game trees using the command:
python autograder.py -q q4
Debugging on these small and manageable test cases is recommended and will help
you to find bugs quickly.
Once your algorithm is working on small trees, you can observe its success in Pacman.
Random ghosts are of course not optimal minimax agents, and so modeling them with
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minimax search may not be appropriate. ExpectimaxAgent, 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 an adversary
which chooses amongst their getLegalActions uniformly at random.
To see how the ExpectimaxAgent behaves in Pacman, run:
python pacman.py -p ExpectimaxAgent -l minimaxClassic -a depth=3
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 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.
The correct implementation of expectimax will lead to Pacman losing some of the tests.
This is not a problem: as it is correct behaviour, it will pass the tests.
Question 5 (6 points): Evaluation Function
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 one random ghost 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).
Grading: the autograder will run your agent on the smallClassic layout 10 times. We
will assign points to your evaluation function in the following way:
If you win at least once without timing out the autograder, you receive 1 points. Any
agent not satisfying these criteria will receive 0 points. +1 for winning at least 5 times, +2
for winning all 10 times +1 for an average score of at least 500, +2 for an average score of
at least 1000 (including scores on lost games) +1 if your games take on average less than
30 seconds on the autograder machine, when run with --no-graphics. The autograder
is run on attu, so this machine will have a fair amount of resources, but your personal
computer could be far less performant (netbooks) or far more performant (gaming rigs).
The additional points for average score and computation time will only be awarded if
you win at least 5 times. You can try your agent out under these conditions with
python autograder.py -q q5
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To run it without graphics, use:
python autograder.py -q q5 --no-graphics
Submission
In order to submit your project, please upload multiAgents.py file. Please do not
upload the files in a zip file or a directory.

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