public void visualizeFunctions() {
PuddleMapFV fvgen = new PuddleMapFV(this.puddleMap, 5, 20, 20);
LinearStateDifferentiableRF rf = new LinearStateDifferentiableRF(fvgen, fvgen.getDim());
LinearStateDifferentiableRF objectiveRF =
new LinearStateDifferentiableRF(fvgen, fvgen.getDim());
objectiveRF.setParameters(new double[] {1., -10, -10, 0, -10, 0, 0, 0, 0, 0});
java.util.List<EpisodeAnalysis> eas =
EpisodeAnalysis.parseFilesIntoEAList(this.expertDir, domain, this.sp);
int depth = 6;
double beta = 10;
DifferentiableSparseSampling dss =
new DifferentiableSparseSampling(
domain,
rf,
new NullTermination(),
0.99,
new NameDependentStateHashFactory(),
depth,
-1,
beta);
// DifferentiableZeroStepPlanner dss = new DifferentiableZeroStepPlanner(domain, rf);
dss.toggleDebugPrinting(false);
MLIRLRequest request = new MLIRLRequest(domain, dss, eas, rf);
request.setBoltzmannBeta(beta);
// MLIRL irl = new MLIRL(request, 0.001, 0.01, 10); //use this for only the given features
MLIRL irl = new MLIRL(request, 0.00001, 0.01, 10);
// MLIRL irl = new MLIRL(request, 0.0001, 0.01, 10);
irl.performIRL();
TerminalFunction tf = new GridWorldTerminalFunction(20, 20);
ValueIteration vi =
new ValueIteration(
this.domain,
objectiveRF,
new NullTermination(),
0.99,
new DiscreteStateHashFactory(),
0.01,
200);
// vi.planFromState(this.initialState);
SparseSampling ssLearned =
new SparseSampling(
this.domain,
request.getRf(),
new NullTermination(),
0.99,
new DiscreteStateHashFactory(),
depth,
-1);
SparseSampling ssObjective =
new SparseSampling(
this.domain,
objectiveRF,
new NullTermination(),
0.99,
new DiscreteStateHashFactory(),
depth,
-1);
StateRewardFunctionValue objectiveRFVis =
new StateRewardFunctionValue(this.domain, objectiveRF);
StateRewardFunctionValue learnedRFVis = new StateRewardFunctionValue(this.domain, rf);
List<State> allStates =
StateReachability.getReachableStates(
this.initialState, (SADomain) this.domain, new DiscreteStateHashFactory());
ValueFunctionVisualizerGUI gui =
GridWorldDomain.getGridWorldValueFunctionVisualization(allStates, ssObjective, null);
StateValuePainter2D vpainter = (StateValuePainter2D) gui.getSvp();
vpainter.toggleValueStringRendering(false);
LandmarkColorBlendInterpolation colorBlend = new LandmarkColorBlendInterpolation();
colorBlend.addNextLandMark(0., Color.BLACK);
colorBlend.addNextLandMark(1., Color.WHITE);
vpainter.setColorBlend(colorBlend);
gui.initGUI();
}
public void runVFRFIRL() {
PuddleMapExactFV fvgen = new PuddleMapExactFV(this.puddleMap, 5);
PuddleMapDistOnlyFV vfFvGen = new PuddleMapDistOnlyFV(this.puddleMap, 5, 20, 20);
GridWorldTerminalFunction tf = new GridWorldTerminalFunction(20, 20);
LinearStateDifferentiableRF objectiveRF =
new LinearStateDifferentiableRF(fvgen, fvgen.getDim());
objectiveRF.setParameters(new double[] {1., -10, -10, 0, -10});
// LinearStateDiffVF vinit = new LinearStateDiffVF(vfFvGen, 5);
// DiffVFRF rf = new DiffVFRF(objectiveRF, vinit);
LinearDiffRFVInit rfvf = new LinearDiffRFVInit(fvgen, vfFvGen, 5, 5);
java.util.List<EpisodeAnalysis> eas =
EpisodeAnalysis.parseFilesIntoEAList(this.expertDir, domain, this.sp);
int depth = 4;
double beta = 10;
DifferentiableSparseSampling dss =
new DifferentiableSparseSampling(
domain,
rfvf,
new NullTermination(),
0.99,
new NameDependentStateHashFactory(),
depth,
-1,
beta);
dss.setValueForLeafNodes(rfvf);
dss.toggleDebugPrinting(false);
MLIRLRequest request = new MLIRLRequest(domain, dss, eas, rfvf);
request.setBoltzmannBeta(beta);
MLIRL irl = new MLIRL(request, 0.001, 0.01, 10); // use this for only the given features
// MLIRL irl = new MLIRL(request, 0.00001, 0.01, 10);
// MLIRL irl = new MLIRL(request, 0.0001, 0.01, 10);
irl.performIRL();
// System.out.println(this.getFVAndShapeString(rf.getParameters()));
String baseName = "SSRFVFD3";
SparseSampling ss =
new SparseSampling(
domain,
rfvf,
new NullTermination(),
0.99,
new NameDependentStateHashFactory(),
depth,
-1);
ss.toggleDebugPrinting(false);
ss.setValueForLeafNodes(rfvf);
// Policy p = new GreedyQPolicy(dss);
Policy p = new GreedyQPolicy(ss);
State simple = this.initialState.copy();
GridWorldDomain.setAgent(simple, 18, 0);
EpisodeAnalysis trainedEp1 = p.evaluateBehavior(simple, objectiveRF, tf, 200);
trainedEp1.writeToFile(trainedDir + "/IRL" + baseName + "EpSimple", this.sp);
State hardAgent = this.initialState.copy();
GridWorldDomain.setAgent(hardAgent, 0, 9);
EpisodeAnalysis trainedEp2 = p.evaluateBehavior(hardAgent, objectiveRF, tf, 200);
trainedEp2.writeToFile(trainedDir + "/IRL" + baseName + "EpHardAgent", this.sp);
dss.resetPlannerResults();
ss.resetPlannerResults();
int ngx = 12;
int ngy = 14;
tf = new GridWorldTerminalFunction(ngx, ngy);
// dss.setTf(tf);
this.puddleMap[ngx][ngy] = 1;
this.puddleMap[20][20] = 0;
vfFvGen.setGoal(ngx, ngy);
State hardGoal = this.initialState.copy();
GridWorldDomain.setAgent(hardGoal, 0, 0);
EpisodeAnalysis trainedEp3 = p.evaluateBehavior(hardGoal, objectiveRF, tf, 200);
trainedEp3.writeToFile(trainedDir + "/IRL" + baseName + "EpHardGoal", this.sp);
new EpisodeSequenceVisualizer(this.v, this.domain, this.sp, this.trainedDir);
}