How to use

MCState

in

burlap.domain.singleagent.mountaincar

@Override
public State copy() {
  return new MCState(x, v);
}

@Override
public State generateState() {
  
  double rx = this.rand.nextDouble() * (this.xmax - this.xmin) + this.xmin;
  double rv = this.rand.nextDouble() * (this.vmax - this.vmin) + this.vmin;
  
  MCState s = new MCState(rx, rv);
  
  return s;
}

public MCState valleyState(){
  return new MCState(this.valleyPos(), 0.);
}

public static void main(String[] args) {
  MountainCar mcGen = new MountainCar();
  SADomain domain = mcGen.generateDomain();
  StateGenerator rStateGen = new MCRandomStateGenerator(mcGen.physParams);
  SARSCollector collector = new SARSCollector.UniformRandomSARSCollector(domain);
  SARSData dataset = collector.collectNInstances(rStateGen, domain.getModel(), 5000, 20, null);
  NormalizedVariableFeatures features = new NormalizedVariableFeatures()
      .variableDomain("x", new VariableDomain(mcGen.physParams.xmin, mcGen.physParams.xmax))
      .variableDomain("v", new VariableDomain(mcGen.physParams.vmin, mcGen.physParams.vmax));
  FourierBasis fb = new FourierBasis(features, 4);
  LSPI lspi = new LSPI(domain, 0.99, new DenseCrossProductFeatures(fb, 3), dataset);
  Policy p = lspi.runPolicyIteration(30, 1e-6);
  Visualizer v = MountainCarVisualizer.getVisualizer(mcGen);
  VisualActionObserver vob = new VisualActionObserver(v);
  vob.initGUI();
  SimulatedEnvironment env = new SimulatedEnvironment(domain,
      new MCState(mcGen.physParams.valleyPos(), 0));
  EnvironmentServer envServ = new EnvironmentServer(env, vob);
  for(int i = 0; i < 100; i++){
    PolicyUtils.rollout(p, envServ);
    envServ.resetEnvironment();
  }
  System.out.println("Finished");
}

public static void MCLSPIFB(){
  MountainCar mcGen = new MountainCar();
  SADomain domain = mcGen.generateDomain();
  StateGenerator rStateGen = new MCRandomStateGenerator(mcGen.physParams);
  SARSCollector collector = new SARSCollector.UniformRandomSARSCollector(domain);
  SARSData dataset = collector.collectNInstances(rStateGen, domain.getModel(), 5000, 20, null);
  NormalizedVariableFeatures inputFeatures = new NormalizedVariableFeatures()
      .variableDomain("x", new VariableDomain(mcGen.physParams.xmin, mcGen.physParams.xmax))
      .variableDomain("v", new VariableDomain(mcGen.physParams.vmin, mcGen.physParams.vmax));
  FourierBasis fb = new FourierBasis(inputFeatures, 4);
  LSPI lspi = new LSPI(domain, 0.99, new DenseCrossProductFeatures(fb, 3), dataset);
  Policy p = lspi.runPolicyIteration(30, 1e-6);
  Visualizer v = MountainCarVisualizer.getVisualizer(mcGen);
  VisualActionObserver vob = new VisualActionObserver(v);
  vob.initGUI();
  SimulatedEnvironment env = new SimulatedEnvironment(domain, new MCState(mcGen.physParams.valleyPos(), 0.));
  env.addObservers(vob);
  for(int i = 0; i < 5; i++){
    PolicyUtils.rollout(p, env);
    env.resetEnvironment();
  }
  System.out.println("Finished");
}

MCState s = new MCState(mcGen.physParams.valleyPos(), 0.);