diff --git a/AMOEBAonAMAK/src/agents/head/Head.java b/AMOEBAonAMAK/src/agents/head/Head.java
index 5b1bbcbe615de4f8a1d3efbbe0631decb7b85b4c..ce3ab15712a6ebac55b3bedddb3823ef348903b1 100644
--- a/AMOEBAonAMAK/src/agents/head/Head.java
+++ b/AMOEBAonAMAK/src/agents/head/Head.java
@@ -111,14 +111,19 @@ public class Head extends AmoebaAgent {
if(getAmas().isReinforcement()) {
+
+ int nb=0;
Double meanNeighborsLastPredictions = null;
+ ArrayList<Context> usedNeighbors = new ArrayList<Context>();
+
if(activatedNeighborsContexts.size()>0) {
- int nb=0;
+
meanNeighborsLastPredictions = 0.0;
for (Context ctxt : activatedNeighborsContexts) {
if(ctxt.lastPrediction != null) {
+ usedNeighbors.add(ctxt);
meanNeighborsLastPredictions += ctxt.lastPrediction;
nb++;
}
@@ -133,7 +138,23 @@ public class Head extends AmoebaAgent {
}
if(meanNeighborsLastPredictions != null) {
getAmas().data.oracleValue = (getAmas().data.oracleValue + meanNeighborsLastPredictions)/2;
+
+ if(getAmas().data.oracleValue>0) {
+
+ System.out.println("#################################################################");
+ System.out.println("PCT " + getAmas().getPerceptionsAndActionState());
+ System.out.println(getAmas().data.oracleValue);
+ for(Context ctxt : usedNeighbors) {
+ System.out.println(ctxt.getName() + " " + ctxt.lastPrediction);
+ }
+ System.out.println(usedNeighbors.size() + " " + nb);
+
+
+ }
}
+
+
+
}
@@ -182,11 +203,9 @@ public class Head extends AmoebaAgent {
getAmas().data.executionTimes[1]=System.currentTimeMillis()- getAmas().data.executionTimes[1];
getAmas().data.executionTimes[2]=System.currentTimeMillis();
- if(getAmas().isReinforcement()) {
- selfAnalysationOfContextsReinforcement();
- }else {
- selfAnalysationOfContexts4();
- }
+
+ selfAnalysationOfContexts4();
+
getAmas().data.executionTimes[2]=System.currentTimeMillis()- getAmas().data.executionTimes[2];
getEnvironment().trace(TRACE_LEVEL.DEBUG, new ArrayList<String>(Arrays.asList("bestContext != null 2", "" + (bestContext != null))));
@@ -233,12 +252,24 @@ public class Head extends AmoebaAgent {
}
globalConfidence = globalConfidence / getAmas().getContexts().size();
+
if (activatedNeighborsContexts.size() > 1) {
-
+
+ double bestNeighborLastPrediction = Double.NEGATIVE_INFINITY;
+ Context bestNeighbor = null;
int i = 1;
for (Context ctxt : activatedNeighborsContexts) {
+
+
+ if(ctxt.lastPrediction> bestNeighborLastPrediction) {
+ bestNeighborLastPrediction = ctxt.lastPrediction;
+ bestNeighbor = ctxt;
+ }
+
+
+
for (Context otherCtxt : activatedNeighborsContexts.subList(i, activatedNeighborsContexts.size())) {
@@ -259,8 +290,17 @@ public class Head extends AmoebaAgent {
}
i++;
+
+
+
}
+
+ getAmas().data.higherNeighborLastPredictionPercepts = new HashMap<String, Double>();
+ for(Percept pct : getAmas().getPercepts()) {
+ getAmas().data.higherNeighborLastPredictionPercepts.put(pct.getName(),bestNeighbor.getRanges().get(pct).getCenter());
+ }
+
}
@@ -973,14 +1013,10 @@ public class Head extends AmoebaAgent {
double currentDistanceToOraclePrediction;
double minDistanceToOraclePrediction = Double.POSITIVE_INFINITY;
- if(getAmas().data.oracleValue > 0) {
- System.out.println("CYCLE " + getAmas().getCycle() + " ORACLE " + getAmas().data.oracleValue + " -------- HEAD 136");
- }
+
for (Context activatedContext : activatedContexts) {
- if(getAmas().data.oracleValue > 0) {
- System.out.println(activatedContext.getName());
- }
+
currentDistanceToOraclePrediction = activatedContext.getLocalModel()
.distance(activatedContext.getCurrentExperiment());
@@ -1057,7 +1093,11 @@ public class Head extends AmoebaAgent {
double minDistanceToOraclePrediction = Double.POSITIVE_INFINITY;
for (Context activatedContext : activatedContexts) {
- //System.out.println(activatedContext.getName());
+
+
+
+
+
currentDistanceToOraclePrediction = activatedContext.getLocalModel()
.distance(activatedContext.getCurrentExperiment());
getAmas().data.distanceToRegression = currentDistanceToOraclePrediction;
@@ -1068,12 +1108,25 @@ public class Head extends AmoebaAgent {
if (!activatedContext.getLocalModel().finishedFirstExperiments()) {
activatedContext.getLocalModel().updateModel(activatedContext.getCurrentExperiment(), getAmas().data.learningSpeed);
getAmas().data.contextNotFinished = true;
+
+ if(getAmas().data.oracleValue>0) {
+
+ System.out.println(activatedContext.getName());
+
+
+ }
}
else if (currentDistanceToOraclePrediction < getAverageRegressionPerformanceIndicator()) {
//else if (currentDistanceToOraclePrediction < regressionPerformance.getPerformanceIndicator()) {
activatedContext.getLocalModel().updateModel(activatedContext.getCurrentExperiment(), getAmas().data.learningSpeed);
+ if(getAmas().data.oracleValue>0) {
+
+ System.out.println(activatedContext.getName());
+
+
+ }
}
if (currentDistanceToOraclePrediction < minDistanceToOraclePrediction) {
@@ -1474,6 +1527,12 @@ public class Head extends AmoebaAgent {
this.getAmas().data.criticity = criticity;
}
+
+ public HashMap<String, Double> getHigherNeighborLastPredictionPercepts() {
+ return getAmas().data.higherNeighborLastPredictionPercepts;
+ }
+
+
/**
* Gets the action.
*
diff --git a/AMOEBAonAMAK/src/experiments/SimpleReinforcement1DSpatialRewardAndAction.java b/AMOEBAonAMAK/src/experiments/SimpleReinforcement1DSpatialRewardAndAction.java
index 09b1c3c3d945174dd9e0eb97250793dd3d073da7..1214086e9abf06e47df5c610bfe6e7d47c469a5f 100644
--- a/AMOEBAonAMAK/src/experiments/SimpleReinforcement1DSpatialRewardAndAction.java
+++ b/AMOEBAonAMAK/src/experiments/SimpleReinforcement1DSpatialRewardAndAction.java
@@ -32,7 +32,7 @@ import utils.XmlConfigGenerator;
public abstract class SimpleReinforcement1DSpatialRewardAndAction {
/* Learn and Test */
public static final int MAX_STEP_PER_EPISODE = 200;
- public static final int N_LEARN = 400;//400
+ public static final int N_LEARN = 1000;//400
public static final int N_TEST = 100;
/* Exploration */
@@ -104,32 +104,37 @@ public abstract class SimpleReinforcement1DSpatialRewardAndAction {
*/
public static class AmoebaQL implements LearningAgent {
public AMOEBA amoebaSpatialReward;
- public AMOEBA amoebaControlModel;
+ //public AMOEBA amoebaControlModel;
public double lr = 0.8;
public double gamma = 0.9;
private Random rand = new Random();
public AmoebaQL() {
amoebaSpatialReward = setupSpatialReward();
- amoebaControlModel = setupControlModel();
+ //amoebaControlModel = setupControlModel();
}
@Override
public HashMap<String, Double> choose(HashMap<String, Double> state, Environment env) {
-// HashMap<String, Double> bestActions = amoebaSpatialReward.maximize(state);
-// double a1 = bestActions.getOrDefault("a1", 0.0);
-// double a2 = bestActions.getOrDefault("a2", 0.0);
-// if(a1 == 0.0) {
-// a1 = rand.nextBoolean() ? -1 : 1;
-// }
-// if(a2 == 0.0) {
-// a2 = rand.nextBoolean() ? -1 : 1;
-// }
-
+// HashMap<String, Double> stateWithVizuAdded = new HashMap<String, Double>(state);
+// stateWithVizuAdded.put("p2", 0.0);
+// stateWithVizuAdded.put("oracle", 0.0);
+// HashMap<String, Double> bestFuturePosition = amoebaSpatialReward.reinforcementRequest(stateWithVizuAdded);
+//
// HashMap<String, Double> action = new HashMap<String, Double>();
-// action.put("a1", a1);
-// action.put("a2", a2);
+// if(bestFuturePosition!=null) {
+// HashMap<String, Double> requestForControlModel = new HashMap<String, Double>();
+// requestForControlModel.put("pCurrent", state.get("p1"));
+// requestForControlModel.put("pGoal", bestFuturePosition.get("p1"));
+//
+// double bestAction = amoebaControlModel.request(requestForControlModel);
+//
+//
+// action.put("a1", bestAction);
+// }
+// action = env.randomAction();
+//
// return action;
return null;
}
@@ -149,12 +154,11 @@ public abstract class SimpleReinforcement1DSpatialRewardAndAction {
- positionAndReward.put("p2",0.0);
- System.out.println("ControlModel " + previousStateCurrentStateAction + " ---------------- SIMPLE REIN XP 149");
- System.out.println("SpatialReward " + positionAndReward + " ---------------- SIMPLE REIN XP 149");
+ //System.out.println("ControlModel " + previousStateCurrentStateAction + " ---------------- SIMPLE REIN XP 149");
+ //System.out.println("SpatialReward " + positionAndReward + " ---------------- SIMPLE REIN XP 149");
amoebaSpatialReward.learn(positionAndReward);
- amoebaControlModel.learn(previousStateCurrentStateAction);
+ //amoebaControlModel.learn(previousStateCurrentStateAction);
}
@@ -352,7 +356,6 @@ public abstract class SimpleReinforcement1DSpatialRewardAndAction {
private static AMOEBA setup() {
ArrayList<Pair<String, Boolean>> sensors = new ArrayList<>();
sensors.add(new Pair<String, Boolean>("p1", false));
- sensors.add(new Pair<String, Boolean>("p2", false));
File config;
try {
config = File.createTempFile("config", "xml");
@@ -380,7 +383,6 @@ public abstract class SimpleReinforcement1DSpatialRewardAndAction {
private static AMOEBA setupSpatialReward() {
ArrayList<Pair<String, Boolean>> sensors = new ArrayList<>();
sensors.add(new Pair<String, Boolean>("p1", false));
- sensors.add(new Pair<String, Boolean>("p2", false));
File config;
try {
config = File.createTempFile("configSpatialReward", "xml");
@@ -398,13 +400,15 @@ public abstract class SimpleReinforcement1DSpatialRewardAndAction {
amoeba.saver = new SaveHelperDummy();
- for(Percept pct : amoeba.getPercepts()) {
- pct.setMax(10);
- pct.setMin(-10);
- }
+// for(Percept pct : amoeba.getPercepts()) {
+// pct.setMax(10);
+// pct.setMin(-10);
+// }
amoeba.setLocalModel(TypeLocalModel.MILLER_REGRESSION);
amoeba.getEnvironment().setMappingErrorAllowed(0.025);
+ amoeba.setReinforcement(true);
+
return amoeba;
}
diff --git a/AMOEBAonAMAK/src/experiments/SimpleReinforcement2DSpatialRewardAndAction.java b/AMOEBAonAMAK/src/experiments/SimpleReinforcement2DSpatialRewardAndAction.java
index a43d156ff8ab300410fd3e8d8ca642a72589c0f8..b526e211e3d00a09e8a03fe091cb830547f8f59d 100644
--- a/AMOEBAonAMAK/src/experiments/SimpleReinforcement2DSpatialRewardAndAction.java
+++ b/AMOEBAonAMAK/src/experiments/SimpleReinforcement2DSpatialRewardAndAction.java
@@ -384,6 +384,8 @@ public abstract class SimpleReinforcement2DSpatialRewardAndAction {
pct.setMin(-10);
}
+ amoeba.setReinforcement(true);
+
return amoeba;
}
diff --git a/AMOEBAonAMAK/src/gui/ContextRendererFX.java b/AMOEBAonAMAK/src/gui/ContextRendererFX.java
index 380baf9679ac64ead18176e965a65417223c39e8..23f4160219d97d02da8a8ac0d9914b99ed48b854 100644
--- a/AMOEBAonAMAK/src/gui/ContextRendererFX.java
+++ b/AMOEBAonAMAK/src/gui/ContextRendererFX.java
@@ -67,7 +67,11 @@ public class ContextRendererFX extends RenderStrategy {
g = 0.0;
b = 1.0;
}
-
+ if(context.lastPrediction > 10000) {
+ r = 1.0;
+ g = 1.0;
+ b = 0.0;
+ }
drawable.setColor(new Color(r, g, b, 200d / 255d));
diff --git a/AMOEBAonAMAK/src/kernel/AMOEBA.java b/AMOEBAonAMAK/src/kernel/AMOEBA.java
index 48e1c2d52f2fe98dc8a98cbb9adb8b053c257a15..3eef319e9653a6fef3c5c8916ef840a84979110b 100644
--- a/AMOEBAonAMAK/src/kernel/AMOEBA.java
+++ b/AMOEBAonAMAK/src/kernel/AMOEBA.java
@@ -181,7 +181,7 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
@Override
protected void onSystemCycleBegin() {
if (cycle % 1000 == 0) {
- Log.defaultLog.inform("AMOEBA", "Cycle " + cycle + ". Nb agents: "+getAgents().size());
+ //Log.defaultLog.inform("AMOEBA", "Cycle " + cycle + ". Nb agents: "+getAgents().size());
}
if(isRenderUpdate()) {
@@ -218,6 +218,7 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
toKillContexts.clear();
lastModifiedContext.clear();
alteredContexts.clear();
+ data.higherNeighborLastPredictionPercepts=null;
}
synchronized private void incrementCycleWithoutRender() {
@@ -393,7 +394,7 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
}
- public double reinfocementRequest(HashMap<String, Double> perceptionsActionState) {
+ public HashMap<String, Double> reinforcementRequest(HashMap<String, Double> perceptionsActionState) {
boolean usingOracle = isUseOracle();
if (usingOracle)
head.changeOracleConnection();
@@ -404,7 +405,7 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
if (usingOracle)
head.changeOracleConnection();
studiedSystem = ss;
- return getAction();
+ return getHigherNeighborLastPredictionPercepts();
}
@Override
@@ -550,8 +551,8 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
- public double getHigherNeighborPrediction() {
- return head.getAction();
+ public HashMap<String, Double> getHigherNeighborLastPredictionPercepts() {
+ return head.getHigherNeighborLastPredictionPercepts();
}
public ArrayList<Context> getContexts() {
diff --git a/AMOEBAonAMAK/src/kernel/AmoebaData.java b/AMOEBAonAMAK/src/kernel/AmoebaData.java
index d843af0d4cec13c5e5f121d0c9e9de0570364df9..56c0503461b1498a9f9cf26e941897129c2e58e8 100644
--- a/AMOEBAonAMAK/src/kernel/AmoebaData.java
+++ b/AMOEBAonAMAK/src/kernel/AmoebaData.java
@@ -19,6 +19,7 @@ public class AmoebaData implements Serializable {
public int numberOfCriticityValuesForAverageforVizualisation = 300;
public Double prediction;
+ public HashMap<String, Double> higherNeighborLastPredictionPercepts = null;
public Double endogenousPredictionActivatedContextsOverlaps = 0.0;
public Double endogenousPredictionActivatedContextsOverlapsWorstDimInfluence = 0.0;
public Double endogenousPredictionActivatedContextsOverlapsInfluenceWithoutConfidence = 0.0;