// Executes the given action
public void executeAction(Action a) {
// For each state variable, retrieve the DD for it, evaluate it
// w.r.t. the current state and build a new state.
ArrayList new_state = new ArrayList();
int c;
for (c = 0; c < (_nVars << 1); c++) {
new_state.add("-");
}
for (c = 0; c < (_nVars << 1); c++) {
Object cur_assign = _state.get(c);
if (cur_assign instanceof Boolean) {
// System.out.println(a._tmID2DD);
int nonprime_id = c + 1;
int prime_id = nonprime_id - _nVars;
Object DD = a._tmID2DD.get(new Integer(prime_id));
_state.set(prime_id - 1, TRUE);
double p_true = _mdp._context.evaluate(DD, _state);
// System.out.println("ID: " + nonprime_id + ", " + prime_id + ": " +
// _mdp._context.printNode(DD) + " -> " +
// _mdp._df.format(p_true));
new_state.set(c, (_r.nextFloat() < p_true) ? TRUE : FALSE);
}
}
_state = new_state;
}
// Derives QFunctions for the given value function and simulates the
// greedy policy for the given number of trials and steps per trial.
// Returns final value of every trial.
public ArrayList simulate(int trials, int steps, long rand_seed) {
ArrayList values = new ArrayList();
_r = new Random(rand_seed);
for (int trial = 1; trial <= trials; trial++) {
System.out.println("\n -----------\n Trial " + trial + "\n -----------");
// Initialize state
_state = new ArrayList();
_nVars = _mdp._alVars.size();
for (int c = 0; c < (_nVars << 1); c++) {
_state.add("-");
}
Iterator i = _mdp._alVars.iterator();
_vars = new TreeSet();
while (i.hasNext()) {
String s = (String) i.next();
if (!s.endsWith("\'")) {
Integer gid = (Integer) _mdp._tmVar2ID.get(s);
_vars.add(gid);
// Note: assign level (level is gid-1 b/c gids in order)
_state.set(gid.intValue() - 1, _r.nextBoolean() ? TRUE : FALSE);
}
}
// System.out.println(_mdp._context.printNode(_mdp._valueDD) + "\n" + _state);
double reward = _mdp._context.evaluate(_mdp._rewardDD, _state);
System.out.print(" " + PrintState(_state) + " " + MDP._df.format(reward));
// Run steps
for (int step = 1; step <= steps; step++) {
// Get action
Action a;
if (_bUseBasis) {
a = getBasisAction();
} else {
a = getAction();
}
// Execute action
executeAction(a);
// Update reward
reward =
(_mdp._bdDiscount.doubleValue() * reward)
+ _mdp._context.evaluate(_mdp._rewardDD, _state);
System.out.println(", a=" + a._sName);
System.out.print(
" " + PrintState(_state) + " " + MDP._df.format(reward) + ": " + "Step " + step);
}
values.add(new Double(reward));
System.out.println();
}
return values;
}
