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PacmanML

This is my implementation of a Q-Learning agent, found in classifierAgents.py, and a Decision-Tree classifier agent, found in mlLearningAgents.py, in the Berkeley Pac-Man environment. The main branch contains the Decision-Tree classifier agent and respective unit tests, and the master branch contains the Q-Learning agent.

Requirements

  • Python 2.7
  • Numpy

Usage

In your command line enter:

python pacman.py -p QLearnAgent -x 2000 -n 2010 -l smallGrid

This selects the agent QLearnAgent, to train on 2000 games, and run on an additional 10 games (2010-2000=10), on smallGrid layout.

When you've tried this, check out his performance on testClassic.

python pacman.py -p QLearnAgent -x 2000 -n 2010 -l testClassic