add TD algorithms and started adopting to continous tasks

- add Q-Learning and SARSA
- more config variables

.idea/codeStyles/codeStyleConfig.xml

@@ -0,0 +1,5 @@
+<component name="ProjectCodeStyleConfiguration">
+  <state>
+    <option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" />
+  </state>
+</component>

src/main/java/core/LearningConfig.java

@@ -2,6 +2,9 @@ package core;
 
 public class LearningConfig {
     public static final int DEFAULT_DELAY = 30;
+    public static final int DEFAULT_NR_OF_EPISODES = 10000;
     public static final float DEFAULT_EPSILON = 0.1f;
     public static final float DEFAULT_DISCOUNT_FACTOR = 1.0f;
+    // Learning rate
+    public static final float DEFAULT_ALPHA = 0.9f;
 }

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

@@ -2,6 +2,7 @@ package core.algo;
 
 import core.DiscreteActionSpace;
 import core.Environment;
+import core.LearningConfig;
 import core.StepResult;
 import core.listener.LearningListener;
 import lombok.Getter;
@@ -16,7 +17,7 @@ import java.util.concurrent.atomic.AtomicInteger;
 
 public abstract class EpisodicLearning<A extends Enum> extends Learning<A> implements Episodic {
     @Setter
-    protected int currentEpisode;
+    protected int currentEpisode = 0;
     protected volatile AtomicInteger episodesToLearn = new AtomicInteger(0);
     @Getter
     protected volatile int episodePerSecond;
@@ -81,7 +82,7 @@ public abstract class EpisodicLearning<A extends Enum> extends Learning<A> imple
 
     @Override
     public void learn(){
-      // TODO remove or learn with default episode number
+        learn(LearningConfig.DEFAULT_NR_OF_EPISODES);
     }
 
     private void startLearning(){
@@ -132,6 +133,15 @@ public abstract class EpisodicLearning<A extends Enum> extends Learning<A> imple
         return episodesToLearn.get();
     }
 
+
+    public int getCurrentEpisode() {
+        return currentEpisode;
+    }
+
+    public int getEpisodesPerSecond() {
+        return episodePerSecond;
+    }
+
     @Override
     public synchronized void save(ObjectOutputStream oos) throws IOException {
       super.save(oos);

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

@@ -30,21 +30,20 @@ import java.util.*;
  *
  * @param <A>
  */
-public class MonteCarloOnPolicyEGreedy<A extends Enum> extends EpisodicLearning<A> {
+public class MonteCarloControlEGreedy<A extends Enum> extends EpisodicLearning<A> {
 
     private Map<Pair<State, A>, Double> returnSum;
     private Map<Pair<State, A>, Integer> returnCount;
 
-    public MonteCarloOnPolicyEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay) {
+    public MonteCarloControlEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay) {
         super(environment, actionSpace, discountFactor, delay);
-        currentEpisode = 0;
         this.policy = new EpsilonGreedyPolicy<>(epsilon);
         this.stateActionTable = new DeterministicStateActionTable<>(this.actionSpace);
         returnSum = new HashMap<>();
         returnCount = new HashMap<>();
     }
 
-    public MonteCarloOnPolicyEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
+    public MonteCarloControlEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
         this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, delay);
     }
 
@@ -107,16 +106,6 @@ public class MonteCarloOnPolicyEGreedy<A extends Enum> extends EpisodicLearning<
         }
     }
 
-    @Override
-    public int getCurrentEpisode() {
-        return currentEpisode;
-    }
-
-    @Override
-    public int getEpisodesPerSecond(){
-        return episodePerSecond;
-    }
-
     @Override
     public void save(ObjectOutputStream oos) throws IOException {
         super.save(oos);

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

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

src/main/java/core/algo/TD/QLearningOffPolicyTDControl.java

@@ -0,0 +1,68 @@
+package core.algo.td;
+
+import core.*;
+import core.algo.EpisodicLearning;
+import core.policy.EpsilonGreedyPolicy;
+import core.policy.GreedyPolicy;
+import core.policy.Policy;
+
+import java.util.Map;
+
+public class QLearningOffPolicyTDControl<A extends Enum> extends EpisodicLearning<A> {
+    private float alpha;
+    private Policy<A> greedyPolicy = new GreedyPolicy<>();
+
+    public QLearningOffPolicyTDControl(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, float learningRate, int delay) {
+        super(environment, actionSpace, discountFactor, delay);
+        alpha = learningRate;
+        this.policy = new EpsilonGreedyPolicy<>(epsilon);
+        this.stateActionTable = new DeterministicStateActionTable<>(this.actionSpace);
+    }
+
+    public QLearningOffPolicyTDControl(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
+        this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, LearningConfig.DEFAULT_ALPHA, delay);
+    }
+
+    @Override
+    protected void nextEpisode() {
+        State state = environment.reset();
+        try {
+            Thread.sleep(delay);
+        } catch (InterruptedException e) {
+            e.printStackTrace();
+        }
+
+        StepResultEnvironment envResult = null;
+        Map<A, Double> actionValues = null;
+
+
+        sumOfRewards = 0;
+        while(envResult == null || !envResult.isDone()) {
+            actionValues = stateActionTable.getActionValues(state);
+            A action = policy.chooseAction(actionValues);
+
+            // Take a step
+            envResult = environment.step(action);
+            double reward = envResult.getReward();
+            State nextState = envResult.getState();
+            sumOfRewards += reward;
+
+            // Q Update
+            double currentQValue = stateActionTable.getActionValues(state).get(action);
+            // maxQ(S', a);
+            // Using intern "greedy policy" as a helper to determine the highest action-value
+            double highestValueNextState = stateActionTable.getActionValues(nextState).get(greedyPolicy.chooseAction(stateActionTable.getActionValues(nextState)));
+
+            double updatedQValue = currentQValue + alpha * (reward + discountFactor * highestValueNextState - currentQValue);
+            stateActionTable.setValue(state, action, updatedQValue);
+
+            state = nextState;
+            try {
+                Thread.sleep(delay);
+            } catch (InterruptedException e) {
+                e.printStackTrace();
+            }
+            dispatchStepEnd();
+        }
+    }
+}

src/main/java/core/algo/TD/SARSA.java

@@ -0,0 +1,68 @@
+package core.algo.td;
+
+import core.*;
+import core.algo.EpisodicLearning;
+import core.policy.EpsilonGreedyPolicy;
+
+import java.util.Map;
+
+
+public class SARSA<A extends Enum> extends EpisodicLearning<A> {
+    private float alpha;
+
+    public SARSA(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, float learningRate, int delay) {
+        super(environment, actionSpace, discountFactor, delay);
+        alpha = learningRate;
+        this.policy = new EpsilonGreedyPolicy<>(epsilon);
+        this.stateActionTable = new DeterministicStateActionTable<>(this.actionSpace);
+    }
+
+    public SARSA(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
+        this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, LearningConfig.DEFAULT_ALPHA, delay);
+    }
+
+    @Override
+    protected void nextEpisode() {
+        State state = environment.reset();
+        try {
+            Thread.sleep(delay);
+        } catch (InterruptedException e) {
+            e.printStackTrace();
+        }
+
+        StepResultEnvironment envResult = null;
+        Map<A, Double> actionValues = stateActionTable.getActionValues(state);
+        A action = policy.chooseAction(actionValues);
+
+        sumOfRewards = 0;
+        while(envResult == null || !envResult.isDone()) {
+            // Take a step
+            envResult = environment.step(action);
+            sumOfRewards += envResult.getReward();
+
+            State nextState = envResult.getState();
+
+            // Pick next action
+            actionValues = stateActionTable.getActionValues(nextState);
+            A nextAction = policy.chooseAction(actionValues);
+
+            // TD update
+            // target = reward + gamma * Q(nextState, nextAction)
+            double currentQValue = stateActionTable.getActionValues(state).get(action);
+            double nextQValue = stateActionTable.getActionValues(nextState).get(nextAction);
+            double reward = envResult.getReward();
+            double updatedQValue = currentQValue + alpha * (reward + discountFactor * nextQValue - currentQValue);
+            stateActionTable.setValue(state, action, updatedQValue);
+
+            state = nextState;
+            action = nextAction;
+
+            try {
+                Thread.sleep(delay);
+            } catch (InterruptedException e) {
+                e.printStackTrace();
+            }
+            dispatchStepEnd();
+        }
+    }
+}

src/main/java/core/algo/TD/TemporalDifferenceOnPolicy.java

@@ -1,4 +0,0 @@
-package core.algo.TD;
-
-public class TemporalDifferenceOnPolicy {
-}

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

@@ -1,18 +1,19 @@
 package core.controller;
 
-import core.*;
+import core.DiscreteActionSpace;
+import core.Environment;
+import core.LearningConfig;
+import core.ListDiscreteActionSpace;
 import core.algo.EpisodicLearning;
 import core.algo.Learning;
 import core.algo.Method;
-import core.algo.mc.MonteCarloOnPolicyEGreedy;
+import core.algo.mc.MonteCarloControlEGreedy;
-import core.gui.LearningView;
+import core.algo.td.QLearningOffPolicyTDControl;
-import core.gui.View;
+import core.algo.td.SARSA;
 import core.listener.LearningListener;
-import core.listener.ViewListener;
 import core.policy.EpsilonPolicy;
 import lombok.Setter;
 
-import javax.swing.*;
 import java.io.*;
 import java.util.List;
 
@@ -27,6 +28,8 @@ public class RLController<A extends Enum> implements LearningListener {
     @Setter
     protected float discountFactor = LearningConfig.DEFAULT_DISCOUNT_FACTOR;
     @Setter
+    protected float learningRate = LearningConfig.DEFAULT_DISCOUNT_FACTOR;
+    @Setter
     protected float epsilon = LearningConfig.DEFAULT_EPSILON;
     protected Learning<A> learning;
     protected boolean fastLearning;
@@ -45,10 +48,14 @@ public class RLController<A extends Enum> implements LearningListener {
 
     public void start() {
         switch(method) {
-            case MC_ONPOLICY_EGREEDY:
+            case MC_CONTROL_EGREEDY:
-                learning = new MonteCarloOnPolicyEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay);
+                learning = new MonteCarloControlEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay);
                 break;
-            case TD_ONPOLICY:
+            case SARSA_EPISODIC:
+                learning = new SARSA<>(environment, discreteActionSpace, discountFactor, epsilon, learningRate, delay);
+                break;
+            case Q_LEARNING_OFF_POLICY_CONTROL:
+                learning = new QLearningOffPolicyTDControl<>(environment, discreteActionSpace, discountFactor, epsilon, learningRate, delay);
                 break;
             default:
                 throw new IllegalArgumentException("Undefined method");

src/main/java/core/gui/StateActionRow.java

@@ -30,7 +30,6 @@ public class StateActionRow<A extends Enum> extends JTextArea {
 
     protected void refreshLabels(){
         if(state == null || actionValues == null) return;
-        System.out.println("refreshing");
         StringBuilder sb = new StringBuilder(state.toString()).append("\n");
         for(Map.Entry<A, Double> actionValue: actionValues.entrySet()){
             sb.append("\t").append(actionValue.getKey()).append("\t").append(actionValue.getValue()).append("\n");

src/main/java/evironment/antGame/AntState.java

@@ -29,7 +29,6 @@ public class AntState implements State, Visualizable {
     private int computeHash() {
         int hash = 7;
         int prime = 31;
-
         int unknown = 0;
         int diff = 0;
         for (Cell[] cells : knownWorld) {

src/main/java/evironment/jumpingDino/Dino.java

@@ -28,9 +28,11 @@ public class Dino extends RenderObject {
     @Override
     public void tick(){
         // reached max jump height
-        if(y + dy < Config.FRAME_HEIGHT - Config.GROUND_Y -Config.OBSTACLE_SIZE - Config.MAX_JUMP_HEIGHT){
+        int topOfDino = Config.FRAME_HEIGHT - Config.GROUND_Y - Config.DINO_SIZE;
+
+        if(y + dy <= topOfDino - Config.MAX_JUMP_HEIGHT) {
             fall();
-        }else if(y + dy >= Config.FRAME_HEIGHT - Config.GROUND_Y - Config.DINO_SIZE){
+        } else if(y + dy >= topOfDino) {
             inJump = false;
             dy = 0;
             y = Config.FRAME_HEIGHT - Config.GROUND_Y - Config.DINO_SIZE;

src/main/java/evironment/jumpingDino/DinoWorld.java

@@ -56,18 +56,28 @@ public class DinoWorld implements Environment<DinoAction>, Visualizable {
             dino.jump();
         }
 
-        for(int i= 0; i < 5; ++i){
+//        for(int i= 0; i < 5; ++i){
+//            dino.tick();
+//            currentObstacle.tick();
+//            if(currentObstacle.getX() < -Config.OBSTACLE_SIZE){
+//                spawnNewObstacle();
+//            }
+//            comp.repaint();
+//            if(ranIntoObstacle()){
+//                done = true;
+//                break;
+//            }
+//        }
         dino.tick();
         currentObstacle.tick();
         if(currentObstacle.getX() < -Config.OBSTACLE_SIZE) {
             spawnNewObstacle();
         }
-            comp.repaint();
         if(ranIntoObstacle()) {
+            reward = 0;
             done = true;
-                break;
-            }
         }
+
         return new StepResultEnvironment(new DinoStateWithSpeed(getDistanceToObstacle(), getCurrentObstacle().getDx()), reward, done, "");
     }
 

src/main/java/evironment/jumpingDino/gui/DinoWorldComponent.java

@@ -19,7 +19,7 @@ public class DinoWorldComponent extends JComponent {
     protected void paintComponent(Graphics g) {
         super.paintComponent(g);
         g.setColor(Color.BLACK);
-        g.fillRect(0, Config.FRAME_HEIGHT - Config.GROUND_Y, Config.FRAME_WIDTH, 2);
+        g.fillRect(0, Config.FRAME_HEIGHT - Config.GROUND_Y, getWidth(), 2);
 
         dinoWorld.getDino().render(g);
         dinoWorld.getCurrentObstacle().render(g);

src/main/java/example/JumpingDino.java

@@ -12,15 +12,17 @@ public class JumpingDino {
         RNG.setSeed(55);
 
         RLController<DinoAction> rl = new RLControllerGUI<>(
-                new DinoWorld(true, true),
+                new DinoWorld(false, false),
-                Method.MC_ONPOLICY_EGREEDY,
+                Method.Q_LEARNING_OFF_POLICY_CONTROL,
                 DinoAction.values());
 
-        rl.setDelay(100);
+        rl.setDelay(10);
-        rl.setDiscountFactor(1f);
+        rl.setDiscountFactor(0.8f);
-        rl.setEpsilon(0.15f);
+        rl.setEpsilon(0.1f);
-        rl.setNrOfEpisodes(100000);
+        rl.setLearningRate(0.5f);
-
+        rl.setNrOfEpisodes(10000);
         rl.start();
+
+
     }
 }

src/main/java/example/RunningAnt.java

@@ -3,16 +3,17 @@ package example;
 import core.RNG;
 import core.algo.Method;
 import core.controller.RLController;
+import core.controller.RLControllerGUI;
 import evironment.antGame.AntAction;
 import evironment.antGame.AntWorld;
 
 public class RunningAnt {
     public static void main(String[] args) {
-        RNG.setSeed(123);
+        RNG.setSeed(56);
 
-        RLController<AntAction> rl = new RLController<>(
+        RLController<AntAction> rl = new RLControllerGUI<>(
                 new AntWorld(3, 3, 0.1),
-                Method.MC_ONPOLICY_EGREEDY,
+                Method.MC_CONTROL_EGREEDY,
                 AntAction.values());
 
         rl.setDelay(200);