add Every-Visit Monte-Carlo

src/main/java/core/algo/EpisodicLearning.java

@@ -5,7 +5,6 @@ import core.Environment;
 import core.LearningConfig;
 import core.StepResult;
 import core.listener.LearningListener;
-import core.policy.EpsilonGreedyPolicy;
 import example.DinoSampling;
 import lombok.Getter;
 import lombok.Setter;
@@ -125,6 +124,7 @@ public abstract class EpisodicLearning<A extends Enum> extends Learning<A> imple
     private void startLearning(){
         dispatchLearningStart();
         while(episodesToLearn.get() > 0){
+
             dispatchEpisodeStart();
             timestampCurrentEpisode = 0;
             nextEpisode();

src/main/java/core/algo/Method.java

@@ -5,5 +5,5 @@ package core.algo;
  * which RL-algorithm should be used.
  */
 public enum Method {
-    MC_CONTROL_FIRST_VISIT, SARSA_EPISODIC, Q_LEARNING_OFF_POLICY_CONTROL
+    MC_CONTROL_FIRST_VISIT, MC_CONTROL_EVERY_VISIT, SARSA_ON_POLICY_CONTROL, Q_LEARNING_OFF_POLICY_CONTROL
 }

src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java

@@ -3,52 +3,40 @@ package core.algo.mc;
 import core.*;
 import core.algo.EpisodicLearning;
 import core.policy.EpsilonGreedyPolicy;
-import core.policy.GreedyPolicy;
-import core.policy.Policy;
 import org.apache.commons.lang3.tuple.ImmutablePair;
 import org.apache.commons.lang3.tuple.Pair;
 
-import java.io.*;
+import java.io.IOException;
-import java.net.URI;
+import java.io.ObjectInputStream;
-import java.nio.file.Files;
+import java.io.ObjectOutputStream;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.nio.file.StandardOpenOption;
 import java.util.*;
 
 /**
- * TODO: Major problem:
+ * Includes both variants of Monte-Carlo methods
- * StateActionPairs are only unique accounting for their position in the episode.
+ * Default method is First-Visit.
- * For example:
+ * Change to Every-Visit by setting flag "useEveryVisit" in the constructor to true.
- * <p>
- * startingState -> MOVE_LEFT : very first state action in the episode i = 1
- * image the agent does not collect the food and does not drop it onto start, the agent will receive
- * -1 for every timestamp hence (startingState -> MOVE_LEFT) will get a value of -10;
- * <p>
- * BUT image moving left from the starting position will have no impact on the state because
- * the agent ran into a wall. The known world stays the same.
- * Taking an action after that will have the exact same state but a different action
- * making the value of this stateActionPair -9 because the stateAction pair took place on the second
- * timestamp, summing up all remaining rewards will be -9...
- * <p>
- * How to encounter this problem?
- *
  * @param <A>
  */
 public class MonteCarloControlFirstVisitEGreedy<A extends Enum> extends EpisodicLearning<A> {
 
     private Map<Pair<State, A>, Double> returnSum;
     private Map<Pair<State, A>, Integer> returnCount;
+    private boolean isEveryVisit;
 
 
-    public MonteCarloControlFirstVisitEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay) {
+    public MonteCarloControlFirstVisitEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay, boolean useEveryVisit) {
         super(environment, actionSpace, discountFactor, delay);
+        isEveryVisit = useEveryVisit;
         this.policy = new EpsilonGreedyPolicy<>(epsilon);
         this.stateActionTable = new DeterministicStateActionTable<>(this.actionSpace);
         returnSum = new HashMap<>();
         returnCount = new HashMap<>();
     }
 
+    public MonteCarloControlFirstVisitEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay) {
+        this(environment, actionSpace, discountFactor, epsilon, delay, false);
+    }
+
     public MonteCarloControlFirstVisitEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
         this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, delay);
     }
@@ -89,35 +77,47 @@ public class MonteCarloControlFirstVisitEGreedy<A extends Enum> extends Episodic
 
 
         //  System.out.printf("Episode %d \t Reward: %f \n", currentEpisode, sumOfRewards);
-        Set<Pair<State, A>> stateActionPairs = new LinkedHashSet<>();
+        HashMap<Pair<State, A>, List<Integer>> stateActionPairs = new LinkedHashMap<>();
 
+        int firstOccurrenceIndex = 0;
         for(StepResult<A> sr : episode) {
-            stateActionPairs.add(new ImmutablePair<>(sr.getState(), sr.getAction()));
+            Pair<State, A> pair = new ImmutablePair<>(sr.getState(), sr.getAction());
+            if(!stateActionPairs.containsKey(pair)) {
+                List<Integer> l = new ArrayList<>();
+                l.add(firstOccurrenceIndex);
+                stateActionPairs.put(pair, l);
+            }
+
+            /*
+            This is the only difference between First-Visit and Every-Visit.
+            When First-Visit is selected, only the first index of the occurrence is put into the list.
+            When Every-Visit is selected, every following occurrence is saved
+            into the list as well.
+             */
+            else if(isEveryVisit) {
+                stateActionPairs.get(pair).add(firstOccurrenceIndex);
+            }
+            ++firstOccurrenceIndex;
         }
-
         //System.out.println("stateActionPairs " + stateActionPairs.size());
-        for(Pair<State, A> stateActionPair : stateActionPairs) {
+        for(Map.Entry<Pair<State, A>, List<Integer>> entry : stateActionPairs.entrySet()) {
-            int firstOccurenceIndex = 0;
+            Pair<State, A> stateActionPair = entry.getKey();
-            // find first occurance of state action pair
+            List<Integer> firstOccurrences = entry.getValue();
-            for(StepResult<A> sr : episode) {
+            for(Integer firstOccurrencesIdx : firstOccurrences) {
-                if(stateActionPair.getKey().equals(sr.getState()) && stateActionPair.getValue().equals(sr.getAction())) {
+                double G = 0;
-                    break;
+                for(int l = firstOccurrencesIdx; l < episode.size(); ++l) {
+                    G += episode.get(l).getReward() * (Math.pow(discountFactor, l - firstOccurrencesIdx));
                 }
-                firstOccurenceIndex++;
+                // slick trick to add G to the entry.
+                // 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));
             }
-
-            double G = 0;
-            for(int l = firstOccurenceIndex; l < episode.size(); ++l) {
-                G += episode.get(l).getReward() * (Math.pow(discountFactor, l - firstOccurenceIndex));
-            }
-            // slick trick to add G to the entry.
-            // 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));
         }
     }
 
+
     @Override
     public void save(ObjectOutputStream oos) throws IOException {
         super.save(oos);

src/main/java/core/controller/RLController.java

@@ -51,7 +51,11 @@ public class RLController<A extends Enum> implements LearningListener {
             case MC_CONTROL_FIRST_VISIT:
                 learning = new MonteCarloControlFirstVisitEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay);
                 break;
-            case SARSA_EPISODIC:
+            case MC_CONTROL_EVERY_VISIT:
+                learning = new MonteCarloControlFirstVisitEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay, true);
+                break;
+
+            case SARSA_ON_POLICY_CONTROL:
                 learning = new SARSA<>(environment, discreteActionSpace, discountFactor, epsilon, learningRate, delay);
                 break;
             case Q_LEARNING_OFF_POLICY_CONTROL: