Reinforcement Learning q-learning Algorithm Learning-3

Q-learning Source code Analysis.
Import Java.util.random;public class qlearning1{private static final int q_size = 6;    Private static final Double GAMMA = 0.8;    private static final int iterations = 10;    private static final int initial_states[] = new int[] {1, 3, 5, 2, 4, 0}; private static final int r[][] = new int[][] {{-1,-1,-1,-1, 0,-1}, {                                                   -1,-1,-1, 0,-1, 100}, {-1,-1,-1, 0,-1,-1}, {-1, 0, 0,-1, 0,-1}, {0,-1,-1, 0,-1, 10    0}, {-1, 0,-1,-1, 0, 100}};    private static int q[][] = new Int[q_size][q_size];        private static int currentstate = 0;        private static void Train () {initialize ();        Perform training, starting at all initial states. for (int j = 0; J < iterations; J + +) {for (int i = 0; i < q_size;            i++) {episode (Initial_states[i]);        }//I}//J System.out.println ("Q Matrix Values:"); for (int i = 0, i < q_size; i++) {for (int j = 0; J < Q_size; J + +) {Syst            Em.out.print (Q[i][j] + ", \ t");        }//J System.out.print ("\ n");        }//I System.out.print ("\ n");    Return        } private static void Test () {//Perform tests, starting at all initial states.        System.out.println ("Shortest routes from initial states:");            for (int i = 0; i < q_size; i++) {currentstate = initial_states[i];            int newstate = 0;                do {newstate = maximum (CurrentState, true);                System.out.print (CurrentState + ",");            CurrentState = newstate;            }while (CurrentState < 5);        System.out.print ("5\n");   } return; } private static void episode (final int initialstate) {currentstate = initialstate;        Until goal state is reached.        do {chooseanaction ();        }while (CurrentState = = 5);        When currentstate = 5, Run through the set once to convergence.        for (int i = 0; i < q_size; i++) {chooseanaction ();    } return;        } private static void Chooseanaction () {int possibleaction = 0;        Randomly choose a possible action connected to the current state.        Possibleaction = Getrandomaction (q_size);            if (R[currentstate][possibleaction] >= 0) {q[currentstate][possibleaction] = reward (possibleaction);        CurrentState = possibleaction;    } return;        } private static int getrandomaction (final int upperbound) {int action = 0;        Boolean choiceisvalid = false; Randomly choose a possiBLE action connected to the current state.            while (Choiceisvalid = = False) {//Get a random value between 0 (inclusive) and 6 (exclusive).            Action = new Random (). Nextint (Upperbound);            if (R[currentstate][action] >-1) {choiceisvalid = true;    }} return action; } private static void Initialize () {for (int i = 0; i < q_size; i++) {for (int j = 0 ; J < Q_size;            J + +) {Q[i][j] = 0;    }//J}//I return; } private static int maximum (final int state, final Boolean returnindexonly) {//If returnindexonly = Tr        UE, the Q matrix index is returned.        If returnindexonly = False, the Q matrix value is returned.        int winner = 0;        Boolean foundnewwinner = false;        Boolean done = false;            while (!done) {Foundnewwinner = false; for (int i = 0; i < q_SIZE;                    i++) {if (I! = winner) {//Avoid Self-comparison.                        if (Q[state][i] > Q[state][winner]) {winner = i;                    Foundnewwinner = true;            }}} if (Foundnewwinner = = False) {done = true;        }} if (returnindexonly = = True) {return winner;        }else{return Q[state][winner]; }} private static int reward (final int Action) {return (int) (R[currentstate][action] + (GAMMA * Maxi    Mum (Action, false)));        } public static void Main (string[] args) {train ();        Test ();    Return }}

Reinforcement Learning q-learning Algorithm Learning-3