Q Learning Maze Game

I've been learning about reinforcement learning. The simplest version I've heard of is called Q learning. The key to this method is maintaing a Q matrix, which stores information about the quality each state-action pair. Once trained, a player can simply look up their state and take the action with the highest value.

The goal is obviously to train this Q matrix to accurately reflect the game. This can be done by taking moves and updating Q each time in the following way:

As you can see below, when my QLearner is training, it takes random moves every time. I think the more common way to do it is to sometimes take random moves and sometimes take moves according to the current version of Q. That kind of weights the learner toward moves that seem better. It doesn't matter in this case, though. The maze problem is so easy that anything would work.

Here, the reward is simply zero unless the player reaches the goal. At first I set the discount factor, gamma, to be 1. This did not work. Since the random player will always eventually finish the maze, this resulted in every square eventually getting a value of 1. The discount factor means that faster routes to the goal are favored.

import numpy as np
from random import randint

class Maze:
    def __init__(self):
        self.w = 5
        self.h = 5
        p = Tile.player
        b = Tile.block
        e = Tile.empty
        g = Tile.goal
        self.map = [
            [e, e, e, b, g],
            [e, b, e, b, e],
            [e, b, e, e, e],
            [e, b, b, b, b],
            [e, e, e, e, e],
    def __repr__(self):
        returnStr = ""
        for i in range(self.h):
            for j in range(self.w):
                if (j, i) == self.playerPos:
                    returnStr += str(Tile.player)
                    returnStr += str(self.map[i][j])
            returnStr += "\n"
        return returnStr
    def tileAt(self, (x, y)):
        return self.map[y][x]
    def isValidPos(self, (x, y)):
        return all([x >= 0, y >= 0, x < self.w, y < self.h])
    def reset(self):
        self.playerPos = (0, 4)
        self.gameWon = False
    def move(self, dir):
        movePos = addTuple(self.playerPos, dir)
        if self.isValidPos(movePos):
            if self.tileAt(movePos) == Tile.empty:
                self.playerPos = movePos
                return 0
            elif self.tileAt(movePos) == Tile.goal:
                self.playerPos = movePos
                self.gameWon = True
                return 1
                return 0
            return 0

class QLearner:
    def __init__(self, maze):
        self.maze = maze
        self.Q = np.zeros((self.maze.w, self.maze.h, 4))
        self.alpha = 0.1
        self.gamma = 0.9
    def learn(self, n):
        for r in range(n):
            while self.maze.gameWon == False:
                i = randint(0,3)
    def updateQ(self, i):
        x0, y0 = self.maze.playerPos
        moveDir = Dir.moveOptions[i]
        reward = self.maze.move(moveDir)
        x, y = self.maze.playerPos

        maxQ = max(self.Q[x, y, [0,1,2,3]])

        self.Q[x0, y0, i] = self.Q[x0, y0, i] + (self.alpha * (reward + (self.gamma * maxQ) - self.Q[x0, y0, i]))
    def getBestMove(self, (x, y)):
        bestMove = Dir.moveOptions[np.argmax(self.Q[x, y, [0,1,2,3]])]
        return bestMove

def addTuple((a1, a2), (b1, b2)):
    return (a1 + b1, a2 + b2)

class Dir:
    UP = (0,-1)
    DOWN = (0, 1)
    LEFT = (-1, 0)
    RIGHT = (1, 0)
    moveOptions = [UP, RIGHT, DOWN, LEFT]

class Tile:
    player = 'p'
    goal = 'g'
    block = 'X'
    empty = ' '

maze = Maze()
learner = QLearner(maze)

print learner.Q

while maze.gameWon == False:
    print maze

Author | Ben Wiener

Currently a Ph.D. student in Physics at Brown University.