Due July 8th, 11:59pm.
In this project, you will design agents for the classic version of Pac-Man. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design.
The code for this project contains the following files, available as a zip archive.
Key files to read:
||This is the file where you will program. It is where all of the pac-man algorithms 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.|
Files you can ignore:
||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 with your code and comments. Please do not change the other files in this distribution or submit any files other than
multiAgents.py. You are welcomed to write supporting functions as you need them and place them in multiAgents.py. Do not change the existing function names because this will only mess up the autograder. Directions for submitting are on the Programming Project 0 website (same submission process as before).
How to submit: The assignment is to be submitted as follows.
Log onto a corn machine, put your source code into a directory on the Stanford AFS space. Go into the directory that contains your source code. Then type:
You can submit multiple times and we will grade your latest submission -- so feel free to submit a lot. In fact why don't you try submitting right now (yes now). If you have problems submitting, please contact the TAs immediately. You will not get extensions because you waited until after the deadline to contact the TAs. See submitting for more details.
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 (as usual). 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, as outlined by the honor code.
Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours and piazza are there for your support; please use them. 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.
First, play a game of classic Pac-Man:
Now, run the provided
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.
1. Reflex Agent (3 points)
A reflex agent chooses an action at each choice point by examining its alternatives via an action evaluation function.
Improve the action evaluation function
multiAgents.py to play respectably. The provided reflex agent code has 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
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.
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.
Action Evaluation: The evaluation function you're writing is evaluating state-action pairs; in later parts of the project, you'll be evaluating states.
Inverse: As features, try the reciprocal of important values (such as distance to food) rather than just the values themselves.
Ghosts: you can never have more ghosts than the layout permits.
2. Minimax Agent (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
MultiAgentAgent, which gives access to
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
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
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.
3. Alpha-Beta Agent (5 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
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.
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.
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.
4. Expectimax Agent (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
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.
5. Evaluation Function (3 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
6. Extensions (Optional)
Get creative! Pacman's been doing well so far, but what if things got a bit more challenging? If you are interested, try programming a more advanced Pac-man Agent and see how well it doesn against smarter foes in a trickier maze. In particular, the ghosts will actively chase Pacman instead of wandering around randomly, and the maze features more twists and dead-ends, but also extra pellets to give Pacman a fighting chance.
You're free to have Pacman use any search procedure, search depth, and evaluation function you like. If you are looking for inspiration, an interesting algorithm try Monte Carlo Tree Search. Monte Carlo Tree Search (or MCTS for short) is one of the most popular algorithms for cutting edge General Game Players.
python pacman.py -l contestClassic -p ContestAgent -g DirectionalGhost -q -n 10
Project 1 is done. Go Pac-Man!