Reinforcement Learning Q-learning 算法学习-3

//Q-learning 源码分析。
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, 100},
{-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++)
{
System.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;

// Travel from state to state until goal state is reached.
do
{
chooseAnAction();
}while(currentState == 5);

// When currentState = 5, Run through the set once more for 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 = True, 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 * maximum(Action, false)));
}

public static void main(String[] args)
{
train();
test();
return;
}

}