diff --git a/build.gradle b/build.gradle
index 7ea77b1..8aa69cf 100644
--- a/build.gradle
+++ b/build.gradle
@@ -1,6 +1,5 @@
 plugins {
     id 'java'
-    id 'org.openjfx.javafxplugin' version '0.0.8'
 }
 
 group 'net.lwenstrom.jan'
@@ -19,8 +18,8 @@ dependencies {
     testCompile group: 'junit', name: 'junit', version: '4.12'
     compileOnly 'org.projectlombok:lombok:1.18.10'
     annotationProcessor 'org.projectlombok:lombok:1.18.10'
-}
+    compile 'org.javatuples:javatuples:1.2'
+    // https://mvnrepository.com/artifact/org.javatuples/javatuples
+    compile group: 'org.javatuples', name: 'javatuples', version: '1.2'
 
-javafx {
-    modules = [ 'javafx.controls', 'javafx.fxml' ]
 }
diff --git a/src/main/java/core/algo/MC/MonteCarloOnPolicyEGreedy.java b/src/main/java/core/algo/MC/MonteCarloOnPolicyEGreedy.java
index e9b02c9..f1a6d89 100644
--- a/src/main/java/core/algo/MC/MonteCarloOnPolicyEGreedy.java
+++ b/src/main/java/core/algo/MC/MonteCarloOnPolicyEGreedy.java
@@ -3,7 +3,7 @@ package core.algo.mc;
 import core.*;
 import core.algo.EpisodicLearning;
 import core.policy.EpsilonGreedyPolicy;
-import javafx.util.Pair;
+import org.javatuples.Pair;
 
 import java.io.IOException;
 import java.io.ObjectInputStream;
@@ -82,12 +82,13 @@ public class MonteCarloOnPolicyEGreedy<A extends Enum> extends EpisodicLearning<
         for (StepResult<A> sr : episode) {
             stateActionPairs.add(new Pair<>(sr.getState(), sr.getAction()));
         }
+
         //System.out.println("stateActionPairs " + stateActionPairs.size());
         for (Pair<State, A> stateActionPair : stateActionPairs) {
             int firstOccurenceIndex = 0;
             // find first occurance of state action pair
             for (StepResult<A> sr : episode) {
-                if (stateActionPair.getKey().equals(sr.getState()) && stateActionPair.getValue().equals(sr.getAction())) {
+                if (stateActionPair.getValue0().equals(sr.getState()) && stateActionPair.getValue1().equals(sr.getAction())) {
                     break;
                 }
                 firstOccurenceIndex++;
@@ -101,7 +102,7 @@ public class MonteCarloOnPolicyEGreedy<A extends Enum> extends EpisodicLearning<
             // if the key does not exists, it will create a new entry with G as default value
             returnSum.merge(stateActionPair, G, Double::sum);
             returnCount.merge(stateActionPair, 1, Integer::sum);
-            stateActionTable.setValue(stateActionPair.getKey(), stateActionPair.getValue(), returnSum.get(stateActionPair) / returnCount.get(stateActionPair));
+            stateActionTable.setValue(stateActionPair.getValue0(), stateActionPair.getValue1(), returnSum.get(stateActionPair) / returnCount.get(stateActionPair));
         }
     }
 
diff --git a/src/main/java/core/gui/View.java b/src/main/java/core/gui/View.java
index af63a8f..dce75c6 100644
--- a/src/main/java/core/gui/View.java
+++ b/src/main/java/core/gui/View.java
@@ -3,8 +3,8 @@ package core.gui;
 import core.Environment;
 import core.algo.Learning;
 import core.listener.ViewListener;
-import javafx.util.Pair;
 import lombok.Getter;
+import org.javatuples.Pair;
 import org.knowm.xchart.QuickChart;
 import org.knowm.xchart.XChartPanel;
 import org.knowm.xchart.XYChart;
@@ -132,8 +132,8 @@ public class View<A extends Enum> implements LearningView{
         }else{
             if(learningInfoPanel.isSmoothenGraphSelected()){
                 Pair<List<Integer>, List<Double>> XYvalues = smoothenGraph(rewardHistory);
-                xValues = XYvalues.getKey();
-                yValues = XYvalues.getValue();
+                xValues = XYvalues.getValue0();
+                yValues = XYvalues.getValue1();
             }else{
                 xValues = null;
                 yValues = rewardHistory;