Pacman Qlearning Agent

This script is based on the python script written by Parsons.

What is it about?

The main idea of this script is the implement of Q-learning method in Pacman game in UC Berkeley CS188 Intro to AI.

The script contains a Q-learning agent player class of Pacman game. Note: this is not a solution for the coursework in CS188.

How does it work?

1. Download the reinforcement pack of Pacman game from here

2. Unzip the package and place mlLearningAgents.py inside the directory.

3. Run the training command in the directory. i.e. for 2000 runs of training and 10 runs of playing in a small grid, run:

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

Implementation of Q-learning

The structure of agent class is given: Function getAction() executes while the agent needs to take an action. Function final() is called at the end of every game.

The steps of implying Q-learning in a Pacman game:

1. initialize Q(s,a)
2. take a random action
3. update Q(s,a)
4. choose the action maximises Q or a random action according to Ɛ-greedy function
5. repeat step 3 and 4 until the game ends
6. update Q(s,a) where s is the last state before the end, a is the last action taken

For initiaization we need to add some attributes to the class:

• a dictionary for storing Q(s,a)
• a list records last state
• a list records last action
• a variable stores the score before last action

For storing Q(s,a), a handy structure called Counter can be found in util.py. We can make use of the function argMax() to return the action maximises Q.

Step 2-4 in Q-learning can be included in getAction() function. step 6 could be included in final() function.

getAction():

• observe the reward of state
• update Q(s,a)
• Ɛ-greedy choose action
• update attributes
• return action

final():

• observe reward
• update Q(s,a)
• reset attributes