// Build the MDP and value function, as well as the Q-functions for
// executing the greedy policy.
public MDPSim(String prob_file, String vfun_file) {
_mdp = new MDP(prob_file, DD.TYPE_ADD);
_bUseBasis = vfun_file.equalsIgnoreCase("basis");
if (_bUseBasis) {
_mdp._valueDD = null;
} else {
_mdp._valueDD =
_mdp._context.buildDDFromUnorderedTree(
MDPConverter.ADDFileToTree(vfun_file), _mdp._tmVar2ID);
_qfuns = new HashMap();
Iterator i = _mdp._hmName2Action.entrySet().iterator();
while (i.hasNext()) {
Map.Entry me = (Map.Entry) i.next();
Action a = (Action) me.getValue();
//////////////////////////////////////////////////////////////
// Regress the current value function through each action
//////////////////////////////////////////////////////////////
Object qfun =
_mdp.regress(_mdp._context.remapGIDsInt(_mdp._valueDD, _mdp._hmPrimeRemap), a);
System.out.println("Calculating Q-function for action: " + a._sName);
// System.out.println(_mdp._context.printNode(qfun) + "\n");
_qfuns.put(a._sName, qfun);
}
}
System.out.println(_mdp);
}
// Determines the best action, given the Q-functions
public Action getAction() {
Iterator i = _qfuns.keySet().iterator();
Action best_act = null;
double best_val = -1d;
while (i.hasNext()) {
String act_name = (String) i.next();
Object qfun = _qfuns.get(act_name);
Action a = (Action) _mdp._hmName2Action.get(act_name);
double val = _mdp._context.evaluate(qfun, _state);
// System.out.println(a._sName + " -> " +_mdp._context.printNode(qfun));
// System.out.println(" " + a._sName + " -> " + _mdp._df.format(val));
if (val > best_val) {
best_act = a;
best_val = val;
}
}
// System.out.println(" Chose: " + best_act._sName);
return best_act;
}
