package rlpark.plugin.rltoys.algorithms.predictions.td;

import rlpark.plugin.rltoys.algorithms.traces.ATraces;

import rlpark.plugin.rltoys.algorithms.traces.EligibilityTraceAlgorithm;

import rlpark.plugin.rltoys.algorithms.traces.Traces;

import rlpark.plugin.rltoys.math.vector.RealVector;

import rlpark.plugin.core.api.monitoring.annotations.Monitor;

public class TDLambda extends TD implements EligibilityTraceAlgorithm

{

private static final long serialVersionUID =

8613865620293286722L;

protected final double lambda;

@Monitor

public final Traces e;

double gamma_t;

public TDLambda(double lambda, double gamma, double alpha, int

nbFeatures) {

this(lambda, gamma, alpha, nbFeatures, new ATraces());

}

public TDLambda(double lambda, double gamma, double alpha, int

nbFeatures, Traces prototype) {

super(gamma, alpha, nbFeatures);

this.lambda = lambda;

e = prototype.newTraces(nbFeatures);

}

@Override

protected double initEpisode() {

e.clear();

gamma_t = gamma;

return super.initEpisode();

}

@Override

public double update(RealVector x_t, RealVector x_tp1, double r_

tp1, double gamma_tp1) {

if (x_t == null)

return initEpisode()

v_t = v.dotProduct(x_t);

delta_t = r_tp1 + gamma_tp1 * v.dotProduct(x_tp1) - v_t;

e.update(lambda * gamma_t, x_t);

v.addToSelf(alpha_v * delta_t, e.vect());

gamma_t = gamma_tp1;

return delta_t;

}

@Override

public void resetWeight(int index) {

super.resetWeight(index);

e.vect().setEntry(index, 0);

}

@Override

public Traces traces() {

return e

}}