AI_QLearningAlgorithm/QLearningAlgorithm.cpp at main · SarahAbuirmeileh/AI_QLearningAlgorithm · GitHub

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// Team Members 👩🏻‍💻👩🏻‍💻

// - Sarah Abu irmeileh

// - Asia Shalaldah

#include <iostream>

#include <cmath>

using namespace std;

// Constants for the array dimensions

const int rows = 6;

const int columns = 6;

// The headers for all functions

void printArray(int array[rows][columns]);

void initializeRewardArray(int Reward[rows][columns]);

int getRandomPossibleAction(int state, int Reward[rows][columns]);

void QLearningAlgorithm(int Reward[rows][columns], int QTable[rows][columns], double y, int episodes);

int main (){

// The rows represents the states and the columns represents the actions

int Reward[rows][columns];

// We want to reach F. Hence, F-> is the goal

// We can add 'A' to each row & column to make the result more familiar to the table in the Readme file

// Assigning the values to the Reward array

initializeRewardArray(Reward);

cout << "The Reward array is : " << endl << endl;

printArray(Reward);

cout << endl << endl;

// Initialize the Q-table array and initially all it's values are -1

int QTable[rows][columns];

for (int i = 0; i < rows; ++i) {

for (int j = 0; j < columns; ++j) {

QTable[i][j] = -1;

}

// The # of trials for computer to learn, usually the # is big

int episode = 1000;

// Constant for learning rate

double y = 0.8;

// Seed for random number generator

srand(time(0));

// Apply Q-learning algorithm

QLearningAlgorithm(Reward, QTable, y, episode);

cout << "The Q table after " << episode << " episodes is : " << endl << endl;

printArray(QTable);

cout << endl;

return 0;

}

void printArray(int array[rows][columns]){

for (int i = 0; i < rows; i++){

for (int j = 0; j < columns; j++){

cout << array[i][j] << " ";

}

cout << endl;

}

void initializeRewardArray(int Reward[rows][columns]){

// Assigning the values to the Reward array as the picture in the ReadMe file

for (int i = 0; i < rows; i++){

for (int j = 0; j < columns; j++){

if (j == 5 && (i == 1 || i == 4 || i == 5)){

// If the column is F (If we reach the goal) give it big value

// In other words if there is a direct action from current state to the gaol state

Reward[i][j] = 100;

}else if ((i == 0 && j == 4) || ((i == 1 || i == 2) && ( j == 3)) || (i == 5 && ( j == 1 || j == 4)) ){

// If there is a possible action give it value 0

Reward[i][j] = 0;

}else if (((i == 3) && (j == 1 || j == 2 || j == 4)) || ( i == 4 && ( j == 0 || j == 3))){

// If there is a possible action give it value 0

Reward[i][j] = 0;

}else{

// If there is no possible action (movement) give it -1

Reward[i][j] = -1;

}

void QLearningAlgorithm(int Reward[rows][columns], int QTable[rows][columns], double y, int episodes){

// Loop for all episodes

while(episodes--){

// Choose a random starting state from all states which are represented via rows

int state = rand() % rows;

// Do while the goal is not reached, in this case while the state != 5

while (true){

// Select one random action from this state call it x, this action should be possible

int x = getRandomPossibleAction(state, Reward);

// Get the maximum Q from the x row using QTable

int maximumQ = -1;

for (int i = 0; i < columns; i++){

maximumQ = max(maximumQ, QTable[x][i]);

}

// Update the QTable according to this equation

QTable[state][x] = Reward[state][x] + y * maximumQ;

// Update the state to be the next state which has been chosen randomly

state = x;

if (x == 5){

break;;

}

int getRandomPossibleAction(int state, int Reward[rows][columns]){

// Select random action, this action should be possible

while (true){

int randomAction = rand() % columns;

// If it's possible action break

if (Reward[state][randomAction] != -1 ){

return randomAction;

}

AI_QLearningAlgorithm/QLearningAlgorithm.cpp at main · SarahAbuirmeileh/AI_QLearningAlgorithm · GitHub

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