Esempio n. 1
0
 public double update(RealVector x_t, Action a_t, RealVector x_tp1, Action a_tp1, double r_tp1) {
   if (x_t == null) return initEpisode();
   Action atp1_star = greedy.decide(x_tp1);
   RealVector phi_sa_t = toStateAction.stateAction(x_t, a_t);
   delta = r_tp1 + gamma * greedy.bestActionValue() - theta.dotProduct(phi_sa_t);
   if (a_t == at_star) e.update(gamma * lambda, phi_sa_t);
   else {
     e.clear();
     e.update(0, phi_sa_t);
   }
   theta.addToSelf(alpha * delta, e.vect());
   at_star = atp1_star;
   return delta;
 }
Esempio n. 2
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 public QLearning(
     Action[] actions,
     double alpha,
     double gamma,
     double lambda,
     StateToStateAction toStateAction,
     int nbFeatures,
     Traces prototype) {
   this.alpha = alpha;
   this.gamma = gamma;
   this.lambda = lambda;
   this.toStateAction = toStateAction;
   greedy = new Greedy(this, actions, toStateAction);
   theta = new PVector(nbFeatures);
   e = prototype.newTraces(nbFeatures);
 }
Esempio n. 3
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 private double initEpisode() {
   if (e != null) e.clear();
   delta = 0.0;
   at_star = null;
   return delta;
 }