add fix RNG, add extended interface EpsilonPolicy and move rewardHistory to model instead of view

- only setting the seed of RNG once at the beginning and not reseeding it afterwards. Deep copying
the initial AntWorld to use as blueprint for resetting the world instead of reseeding and creating pesudo random again. Reseeding the RNG has influence action selecting to always
choose the same trajectory.
- instance of is used to determine if policy has epsilon or not and the view will adopt to this, only showing epsilon slider if policy has epsilon

src/main/java/core/algo/MC/MonteCarloOnPolicyEGreedy.java

@@ -4,6 +4,8 @@ import core.*;
 import core.algo.Learning;
 import core.policy.EpsilonGreedyPolicy;
 import javafx.util.Pair;
+import lombok.Setter;
+
 import java.util.*;
 
 /**
@@ -26,13 +28,18 @@ import java.util.*;
  */
 public class MonteCarloOnPolicyEGreedy<A extends Enum> extends Learning<A> {
 
-    public MonteCarloOnPolicyEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace) {
-        super(environment, actionSpace);
-        discountFactor = 1f;
-        this.policy = new EpsilonGreedyPolicy<>(0.1f);
-        this.stateActionTable = new StateActionHashTable<>(actionSpace);
+    public MonteCarloOnPolicyEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay) {
+        super(environment, actionSpace, discountFactor, delay);
+
+        this.policy = new EpsilonGreedyPolicy<>(epsilon);
+        this.stateActionTable = new StateActionHashTable<>(this.actionSpace);
     }
 
+    public MonteCarloOnPolicyEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
+        this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, delay);
+    }
+
+
     @Override
     public void learn(int nrOfEpisodes) {