/*
* input transition exp loop through transition experiences
*
* for each transition t use Q update rule oldState and action comes from t
* new state comes from t
*
* for Q update fill up Q_table (output)
*/
public void Qsweep(List<Transition> transitions) {
if (firstTimeQ) initTable(transitions);
for (int index = 0; index < transitions.size(); index++) {
Transition t = transitions.get(index);
// get reward (if any)
float reward = -0.08f; // small penalty for each timestep used
if (t.getNextState().getEnemyHealth() < 0.001) {
reward = 5;
} else if (t.getNextState().getOgreHealth() < 0.001) {
reward = -10;
} else if ( // t.getNextState().getEnemyHealth() <
// t.getCurrentState()
// .getEnemyHealth()&&
t.getNextState().getOgreEnergy() < t.getCurrentState().getOgreEnergy()) {
// if we just got him with the bow
reward = 5;
// System.out.println("got reward");
}
Q_table[index][0] += alpha * (reward + gamma * queryBestState(t) - Q_table[index][0]);
// original version
if (Q_table[index][0] > 10) Q_table[index][0] = 10;
if (Q_table[index][0] < -10) Q_table[index][0] = -10;
// System.out.println("Q: "+index+": "+Q_tableNN[index][0]);
}
firstTimeQ = false;
}
public double queryBestState(Transition t) {
double energy = t.getNextState().getOgreEnergy();
double distance = t.getNextState().getEnemyDistance();
double vAttack = FQ_learner.nearestValue(energy, distance, 0);
double vEvade = FQ_learner.nearestValue(energy, distance, 1);
if (vAttack > vEvade) return vAttack;
else return vEvade;
}
private void printTrans(Transition t) {
System.out.print(
"trans:"
+ t.getCurrentState().getOgreHealth()
+ " "
+ t.getCurrentState().getOgreEnergy()
+ " "
+ t.getCurrentState().getEnemyHealth()
+ " "
+ t.getCurrentState().getEnemyDistance()
+ " "
+ t.getAction()
+ " "
+ t.getNextState().getOgreHealth()
+ " "
+ t.getNextState().getOgreEnergy()
+ " "
+ t.getNextState().getEnemyHealth()
+ " "
+ t.getNextState().getEnemyDistance());
}
