In this problem, 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 for this project contains the following files, available as multiagent.zip.
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. graphicsDisplay.py : Graphics for Pac-Man graphicsUtils.py : Support for Pac-Man graphics 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
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.
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
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
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 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 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. 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.
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.
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