diff --git a/AMOEBAonAMAK/src/agents/context/localModel/LocalModel.java b/AMOEBAonAMAK/src/agents/context/localModel/LocalModel.java
index a2268693f46e26bb676a1f63ed7c56b8a7ba20db..bb2bec7d64e982e8a3fac65c04a5dd0ed84c56b2 100644
--- a/AMOEBAonAMAK/src/agents/context/localModel/LocalModel.java
+++ b/AMOEBAonAMAK/src/agents/context/localModel/LocalModel.java
@@ -1,6 +1,7 @@
package agents.context.localModel;
import java.util.ArrayList;
+import java.util.HashMap;
import agents.context.Context;
import agents.context.Experiment;
@@ -31,6 +32,7 @@ public abstract class LocalModel {
public abstract double getProposition(Context context);
public abstract double getProposition(Experiment experiment);
public abstract double getMaxProposition(Context context);
+ public abstract HashMap<String, Double> getMax(Context context);
public abstract double getMinProposition(Context context);
diff --git a/AMOEBAonAMAK/src/agents/context/localModel/LocalModelMillerRegression.java b/AMOEBAonAMAK/src/agents/context/localModel/LocalModelMillerRegression.java
index 87998fe51fe7f0fbd06036fa3e26bf74c662f8b9..a27ec497b082cb35ae4cf2563d2e8fca265d509d 100644
--- a/AMOEBAonAMAK/src/agents/context/localModel/LocalModelMillerRegression.java
+++ b/AMOEBAonAMAK/src/agents/context/localModel/LocalModelMillerRegression.java
@@ -2,6 +2,7 @@ package agents.context.localModel;
import java.util.ArrayList;
import java.util.Arrays;
+import java.util.HashMap;
import java.util.List;
import agents.context.Context;
@@ -132,6 +133,35 @@ public class LocalModelMillerRegression extends LocalModel{
return result;
}
+ public HashMap<String, Double> getMax(Context context){
+ ArrayList<Percept> percepts = context.getAmas().getPercepts();
+
+ HashMap<String, Double> result = new HashMap<String, Double>();
+ result.put("oracle", coefs[0]);
+
+ if (coefs[0] == Double.NaN)
+ throw new ArithmeticException("First coeficient of model cannot be NaN");
+
+ for (int i = 1 ; i < coefs.length ; i++) {
+
+ if (Double.isNaN(coefs[i])) coefs[i] = 0.0;
+ if(coefs[i]>0) {
+ Percept p = percepts.get(i-1);
+ double value = coefs[i] * context.getRanges().get(p).getEnd();
+ result.put("oracle", value);
+ result.put(p.getName(), context.getRanges().get(p).getEnd());
+ }
+ else {
+ Percept p = percepts.get(i-1);
+ double value = coefs[i] * context.getRanges().get(p).getStart();
+ result.put("oracle", value);
+ result.put(p.getName(), context.getRanges().get(p).getStart());
+ }
+ }
+
+ return result;
+ }
+
public double getMinProposition(Context context) {
ArrayList<Percept> percepts = context.getAmas().getPercepts();
diff --git a/AMOEBAonAMAK/src/agents/head/Head.java b/AMOEBAonAMAK/src/agents/head/Head.java
index f4de3348de5bde8f9ede9591fa28887989137332..c3070c579b432016e1b7f6d258a2b32b0a8236f8 100644
--- a/AMOEBAonAMAK/src/agents/head/Head.java
+++ b/AMOEBAonAMAK/src/agents/head/Head.java
@@ -17,7 +17,7 @@ import agents.percept.Percept;
import kernel.AMOEBA;
import ncs.NCS;
import utils.Pair;
-import utils.PickRandom;
+import utils.RandomUtils;
import utils.PrintOnce;
import utils.Quadruplet;
import utils.TRACE_LEVEL;
@@ -347,7 +347,7 @@ public class Head extends AmoebaAgent {
// to limit performance impact, we limit our search on a random sample.
// a better way would be to increase neighborhood.
PrintOnce.print("Play without oracle : no nearest context in neighbors, searching in a random sample. (only shown once)");
- List<Context> searchList = PickRandom.pickNRandomElements(getAmas().getContexts(), 100);
+ List<Context> searchList = RandomUtils.pickNRandomElements(getAmas().getContexts(), 100);
nearestContext = this.getNearestContext(searchList);
if(nearestContext != null) {
getAmas().data.prediction = nearestContext.getActionProposal();
diff --git a/AMOEBAonAMAK/src/experiments/SimpleReinforcement.java b/AMOEBAonAMAK/src/experiments/SimpleReinforcement.java
new file mode 100644
index 0000000000000000000000000000000000000000..75c015c32db16918c66f2de5589ce3cc4a12de1a
--- /dev/null
+++ b/AMOEBAonAMAK/src/experiments/SimpleReinforcement.java
@@ -0,0 +1,117 @@
+package experiments;
+
+import java.io.File;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.Random;
+
+import fr.irit.smac.amak.Configuration;
+import fr.irit.smac.amak.ui.drawables.Drawable;
+import fr.irit.smac.amak.ui.drawables.DrawableOval;
+import gui.AmoebaWindow;
+import javafx.scene.paint.Color;
+import kernel.AMOEBA;
+import utils.Pair;
+import utils.RandomUtils;
+import utils.XmlConfigGenerator;
+
+public class SimpleReinforcement {
+
+ private Random rand = new Random();
+ private double x = 0;
+ private double reward = 0;
+ private Drawable pos;
+
+ public static void main(String[] args) {
+ ArrayList<Pair<String, Boolean>> sensors = new ArrayList<>();
+ sensors.add(new Pair<String, Boolean>("p1", false));
+ sensors.add(new Pair<String, Boolean>("a1", true));
+ File config;
+ try {
+ config = File.createTempFile("config", "xml");
+ XmlConfigGenerator.makeXML(config, sensors);
+ } catch (IOException e) {
+ e.printStackTrace();
+ System.exit(1);
+ return; // now compilator know config is initialized
+ }
+
+ AMOEBA amoeba = new AMOEBA(config.getAbsolutePath(), null);
+ SimpleReinforcement env = new SimpleReinforcement();
+
+ Random r = new Random();
+ HashMap<String, Double> state = env.reset();
+ HashMap<String, Double> state2;
+ for(int i = 0; i < 10000; i++) {
+ state.remove("oracle");
+ HashMap<String, Double> action = amoeba.maximize(state);
+ if(r.nextDouble() < 0.1 || action.get("oracle").equals(Double.NEGATIVE_INFINITY) ) {
+ System.out.println("Random action");
+ action.put("a1", (r.nextBoolean() ? 10.0 : -10.0));
+ }
+ state2 = env.step(action.get("a1"));
+ action.put("oracle", state2.get("oracle"));
+ action.put("p1", state.get("p1"));
+ System.out.println(action);
+ amoeba.learn(action);
+ state = state2;
+ }
+ }
+
+ /**
+ * Must be called AFTER an AMOEBA with GUI
+ */
+ public SimpleReinforcement() {
+ Configuration.commandLineMode = false;
+ AmoebaWindow instance = AmoebaWindow.instance();
+ pos = new DrawableOval(0.5, 0.5, 1, 1);
+ pos.setColor(new Color(0.5, 0.0, 0.0, 0.5));
+ instance.mainVUI.add(pos);
+ instance.mainVUI.createAndAddRectangle(-50, -0.25, 100, 0.5);
+ instance.mainVUI.createAndAddRectangle(-0.25, -1, 0.5, 2);
+
+
+
+ }
+
+ public HashMap<String, Double> step(double action){
+ if(action == 0.0) action = rand.nextDouble();
+ if(action > 0.0) action = Math.ceil(action);
+ if(action < 0.0 ) action = Math.floor(action);
+ if(action > 1.0) action = 1.0;
+ if(action < -1.0) action = -1.0;
+ double oldX = x;
+ x = x + action;
+ if(x < -50.0 || x > 50.0) {
+ x = RandomUtils.nextDouble(rand, -50.0, Math.nextUp(50.0));
+ reward = -100.0;
+ } else if(x == 0.0 || sign(oldX) != sign(x)) {
+ // win !
+ reward = 100.0;
+ x = RandomUtils.nextDouble(rand, -50.0, Math.nextUp(50.0));
+ } else {
+ reward = -1.0;
+ }
+ HashMap<String, Double> ret = new HashMap<>();
+ ret.put("p1", x);
+ ret.put("oracle", reward);
+ pos.move(x, 0);
+ return ret;
+ }
+
+ public HashMap<String, Double> reset(){
+ x = RandomUtils.nextDouble(rand, -50.0, Math.nextUp(50.0));
+ reward = 0.0;
+
+ HashMap<String, Double> ret = new HashMap<>();
+ ret.put("p1", x);
+ ret.put("oracle", reward);
+ return ret;
+ }
+
+ private int sign(double x) {
+ return x < 0 ? -1 : 1;
+ }
+
+}
diff --git a/AMOEBAonAMAK/src/gui/ContextRendererFX.java b/AMOEBAonAMAK/src/gui/ContextRendererFX.java
index cfe62c32fd7d5c9909ad1710e570cf216962df7f..75ddf4125607f21b3ef19ca0dcaaea006105be4d 100644
--- a/AMOEBAonAMAK/src/gui/ContextRendererFX.java
+++ b/AMOEBAonAMAK/src/gui/ContextRendererFX.java
@@ -83,7 +83,7 @@ public class ContextRendererFX extends RenderStrategy {
*/
public DrawableRectangle getDrawable() {
if (!context.isDying() && drawable == null) {
- drawable = new DrawableContext(0, 0, 10, 10, context);
+ drawable = new DrawableContext(0, 0, 0, 0, context);
AmoebaWindow.instance().mainVUI.add(drawable);
}
return drawable;
diff --git a/AMOEBAonAMAK/src/kernel/AMOEBA.java b/AMOEBAonAMAK/src/kernel/AMOEBA.java
index d05843c62e72bac2fd2c9abb45de07fb06c2625c..4da9804eb1ce27da404b8c31afe82fe59c63df1f 100644
--- a/AMOEBAonAMAK/src/kernel/AMOEBA.java
+++ b/AMOEBAonAMAK/src/kernel/AMOEBA.java
@@ -429,78 +429,23 @@ public class AMOEBA extends Amas<World> implements IAMOEBA {
if(good) pac.add(c);
}
- ArrayList<Pair<HashMap<String, Double>, Double>> sol = new ArrayList<>();
+ ArrayList<HashMap<String, Double>> sol = new ArrayList<>();
for(Context c : pac) {
- sol.add(maximiseContext(known, percepts, unknown, c));
+ sol.add(c.getLocalModel().getMax(c));
}
HashMap<String, Double> max = new HashMap<>();
- // set default value if no solution
- for(Percept p : unknown) {
- max.put(p.getName(), 0.0);
- }
+
Double maxValue = Double.NEGATIVE_INFINITY;
+ max.put("oracle", maxValue);
//find best solution
- for(Pair<HashMap<String, Double>, Double> s : sol) {
- if(s.getB() > maxValue) {
- maxValue = s.getB();
- max = s.getA();
+ for(HashMap<String, Double> s : sol) {
+ if(s.get("oracle") > maxValue) {
+ maxValue = s.get("oracle");
+ max = s;
}
}
- max.put("oracle", maxValue);
return max;
}
-
- private Pair<HashMap<String, Double>, Double> maximiseDummy(HashMap<String, Double> known,
- ArrayList<Percept> percepts, ArrayList<Percept> unknown, Context c) {
- HashMap<String, Double> res = new HashMap<>();
- for(Percept p : unknown) {
- res.put(p.getName(), c.getRangeByPercept(p).getCenter());
- }
- HashMap<String, Double> tmpReq = new HashMap<>(res);
- HashMap<String, Double> old = perceptions;
- perceptions = tmpReq;
- Pair<HashMap<String, Double>, Double> ret = new Pair<>(res, c.getActionProposal());
- perceptions = old;
- return ret;
- }
-
- //TODO tests !
- private Pair<HashMap<String, Double>, Double> maximiseContext(HashMap<String, Double> known,
- ArrayList<Percept> percepts, ArrayList<Percept> unknown, Context c) {
- HashMap<String, Double> res = new HashMap<>();
-
- Double[] coefs = c.getLocalModel().getCoef();
- double[] vCoefs = new double[coefs.length-1];
- for(int i = 1; i < coefs.length; i++) {
- vCoefs[i-1] = coefs[i];
- }
- LinearObjectiveFunction fct = new LinearObjectiveFunction(vCoefs, coefs[0]);
- ArrayList<LinearConstraint> constraints = new ArrayList<>();
- //TODO : problem : we are not sure that the order of percepts is the same as coefs
- int i = 0;
- for(String p : known.keySet()) {
- double[] cf = new double[percepts.size()];
- cf[i++] = 1.0;
- constraints.add(new LinearConstraint(cf, Relationship.EQ, known.get(p)));
- }
- int unknowStart = i;
- for(Percept p : unknown) {
- double[] cf = new double[percepts.size()];
- cf[i++] = 1.0;
- constraints.add(new LinearConstraint(cf, Relationship.GEQ, c.getRangeByPercept(p).getStart()));
- constraints.add(new LinearConstraint(cf, Relationship.LEQ, c.getRangeByPercept(p).getEnd()));
- }
- SimplexSolver solver = new SimplexSolver();
- LinearConstraintSet set = new LinearConstraintSet(constraints);
- PointValuePair sol = solver.optimize(fct, set, GoalType.MAXIMIZE);
- for(Percept p : unknown) {
- //TODO check if the order match
- res.put(p.getName(), sol.getFirst()[unknowStart++]);
- }
-
- Pair<HashMap<String, Double>, Double> ret = new Pair<>(res, sol.getSecond());
- return ret;
- }
public LocalModel buildLocalModel(Context context) {
switch (localModel) {
diff --git a/AMOEBAonAMAK/src/utils/PickRandom.java b/AMOEBAonAMAK/src/utils/RandomUtils.java
similarity index 63%
rename from AMOEBAonAMAK/src/utils/PickRandom.java
rename to AMOEBAonAMAK/src/utils/RandomUtils.java
index ae94493c4a00b50e6654ab8722eac7f98de66117..9440f17725430ccad2abcee4edb6470d61072134 100644
--- a/AMOEBAonAMAK/src/utils/PickRandom.java
+++ b/AMOEBAonAMAK/src/utils/RandomUtils.java
@@ -5,7 +5,7 @@ import java.util.List;
import java.util.Random;
import java.util.concurrent.ThreadLocalRandom;
-public class PickRandom {
+public class RandomUtils {
/**
* Pick N random element from the list. if n is bigger than the list, return the list.
@@ -40,4 +40,19 @@ public class PickRandom {
public static <E> List<E> pickNRandomElements(List<E> list, int n) {
return pickNRandomElements(list, n, ThreadLocalRandom.current());
}
+
+ /**
+ * Generate a pseudorandom double values, conforming to the given origin (inclusive) and bound(exclusive).
+ * @param rand
+ * @param origin the origin (inclusive) of the random value
+ * @param bound the bound (exclusive) of the random value
+ * @return
+ */
+ public static double nextDouble(Random rand, double origin, double bound) {
+ double r = rand.nextDouble();
+ r = r * (bound - origin) + origin;
+ if (r >= bound) // correct for rounding
+ r = Math.nextDown(bound);
+ return r;
+ }
}