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Implementation of many popular AI algorithms to play the game of Pacman such as Minimax, Expectimax and Greedy.

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AI algorithms for Pacman

Intro

The Pacman Projects by the University of California, Berkeley.

Animated gif pacman game

Start a game with the command and move the agents using ASWD keyboard buttons or arrow keys:

$ python pacman.py

You can see the list of all options and their default values via:

$ python pacman.py -h

Multi-Agent algorithms

  • ReflexAgent: an agent that considers food locations, ghost locations and score to perform well.
$ python pacman.py -p ReflexAgent -l originalClassic -n 1 -ghost DirectionalGhost -z 0.8 -k 1
  • MinimaxAgent: an adversarial search agent implementing minimax algorithm
$ python pacman.py -p MinimaxAgent -l minimaxClassic -a depth=4
  • AlphaBetaAgent: an adversarial search agent implementing minimax algorithm with alpha-beta pruning to more efficiently explore the minimax tree.
$ python pacman.py -p AlphaBetaAgent -l openClassic -a depth=2
  • Expectimax: an adversarial search agent implementing expectimax algorithm
$ python pacman.py -l mediumClassic -p ExpectimaxAgent -a depth=2

Search algorithms

  • DeepSearch: a deep search algorithm to find the best possible path given an evaluation function, it si faster than minimax but doesn't keep into considerations ghosts
$ python pacman.py -l trickyClassic -p DeepSearchAgent -a depth=6 evalFn=evaluationFunction

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