adopt MVC pattern and add real time graph interface

build.gradle

@@ -13,6 +13,9 @@ repositories {
 }
 
 dependencies {
+    // https://mvnrepository.com/artifact/org.jfree/jfreechart
+    // https://mvnrepository.com/artifact/org.knowm.xchart/xchart
+    compile group: 'org.knowm.xchart', name: 'xchart', version: '3.2.2'
     testCompile group: 'junit', name: 'junit', version: '4.12'
     compileOnly 'org.projectlombok:lombok:1.18.10'
     annotationProcessor 'org.projectlombok:lombok:1.18.10'

src/main/java/core/DiscreteActionSpace.java

@@ -3,7 +3,7 @@ package core;
 import java.util.List;
 
 public interface DiscreteActionSpace<A extends Enum> {
-    int getNumberOfAction();
+    int getNumberOfActions();
     void addAction(A a);
     void addActions(A... as);
     List<A> getAllActions();

src/main/java/core/LearningConfig.java

@@ -0,0 +1,7 @@
+package core;
+
+public class LearningConfig {
+    public static final int DEFAULT_DELAY = 1;
+    public static final float DEFAULT_EPSILON = 0.1f;
+    public static final float DEFAULT_DISCOUNT_FACTOR = 1.0f;
+}

src/main/java/core/ListDiscreteActionSpace.java

@@ -32,7 +32,7 @@ public class ListDiscreteActionSpace<A extends Enum> implements DiscreteActionSp
     }
 
     @Override
-    public int getNumberOfAction(){
+    public int getNumberOfActions(){
         return actions.size();
     }
 }

src/main/java/core/algo/Learning.java

@@ -2,26 +2,62 @@ package core.algo;
 
 import core.DiscreteActionSpace;
 import core.Environment;
+import core.LearningConfig;
 import core.StateActionTable;
+import core.listener.LearningListener;
 import core.policy.Policy;
+import lombok.Getter;
+import lombok.Setter;
 
+import javax.swing.*;
+import java.util.HashSet;
+import java.util.Set;
+
+@Getter
 public abstract class Learning<A extends Enum> {
     protected Policy<A> policy;
     protected DiscreteActionSpace<A> actionSpace;
     protected StateActionTable<A> stateActionTable;
     protected Environment<A> environment;
     protected float discountFactor;
+    @Setter
     protected float epsilon;
+    protected Set<LearningListener> learningListeners;
+    @Setter
+    protected int delay;
 
-    public Learning(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon){
+    public Learning(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay){
         this.environment = environment;
         this.actionSpace = actionSpace;
         this.discountFactor = discountFactor;
         this.epsilon = epsilon;
-    }
+        this.delay = delay;
-    public Learning(Environment<A> environment, DiscreteActionSpace<A> actionSpace){
+        learningListeners = new HashSet<>();
-        this(environment, actionSpace, 1.0f, 0.1f);
     }
 
-    public abstract void learn(int nrOfEpisodes, int delay);
+    public Learning(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon){
+        this(environment, actionSpace, discountFactor, epsilon, LearningConfig.DEFAULT_DELAY);
+    }
+
+    public Learning(Environment<A> environment, DiscreteActionSpace<A> actionSpace){
+        this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, LearningConfig.DEFAULT_DELAY);
+    }
+
+    public abstract void learn(int nrOfEpisodes);
+
+    public void addListener(LearningListener learningListener){
+        learningListeners.add(learningListener);
+    }
+
+    protected void dispatchEpisodeEnd(double sum){
+        for(LearningListener l: learningListeners) {
+            l.onEpisodeEnd(sum);
+        }
+    }
+
+    protected void dispatchEpisodeStart(){
+        for(LearningListener l: learningListeners){
+            l.onEpisodeStart();
+        }
+    }
 }

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

@@ -34,22 +34,21 @@ public class MonteCarloOnPolicyEGreedy<A extends Enum> extends Learning<A> {
     }
 
     @Override
-    public void learn(int nrOfEpisodes, int delay) {
+    public void learn(int nrOfEpisodes) {
 
         Map<Pair<State, A>, Double> returnSum = new HashMap<>();
         Map<Pair<State, A>, Integer> returnCount = new HashMap<>();
 
-        State startingState = environment.reset();
         for(int i = 0; i < nrOfEpisodes; ++i) {
             List<StepResult<A>> episode = new ArrayList<>();
             State state = environment.reset();
-            double rewardSum = 0;
+            double sumOfRewards = 0;
             for(int j=0; j < 10; ++j){
                 Map<A, Double> actionValues = stateActionTable.getActionValues(state);
                 A chosenAction = policy.chooseAction(actionValues);
                 StepResultEnvironment envResult = environment.step(chosenAction);
                 State nextState = envResult.getState();
-                rewardSum +=  envResult.getReward();
+                sumOfRewards +=  envResult.getReward();
                 episode.add(new StepResult<>(state, chosenAction, envResult.getReward()));
 
                 if(envResult.isDone()) break;
@@ -57,13 +56,14 @@ public class MonteCarloOnPolicyEGreedy<A extends Enum> extends Learning<A> {
                 state = nextState;
 
                 try {
-                    Thread.sleep(1);
+                    Thread.sleep(delay);
                 } catch (InterruptedException e) {
                     e.printStackTrace();
                 }
             }
 
-            System.out.printf("Episode %d \t Reward: %f \n", i, rewardSum);
+            dispatchEpisodeEnd(sumOfRewards);
+            System.out.printf("Episode %d \t Reward: %f \n", i, sumOfRewards);
             Set<Pair<State, A>> stateActionPairs = new HashSet<>();
 
             for(StepResult<A> sr: episode){

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

@@ -0,0 +1,5 @@
+package core.algo;
+
+public enum Method {
+    MC_ONPOLICY_EGREEDY, TD_ONPOLICY
+}

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

@@ -0,0 +1,81 @@
+package core.controller;
+
+import core.DiscreteActionSpace;
+import core.Environment;
+import core.ListDiscreteActionSpace;
+import core.algo.Learning;
+import core.algo.Method;
+import core.algo.mc.MonteCarloOnPolicyEGreedy;
+import core.gui.View;
+
+import javax.swing.*;
+import java.util.Optional;
+
+public class RLController<A extends Enum> implements ViewListener{
+    protected Environment<A> environment;
+    protected Learning<A> learning;
+    protected DiscreteActionSpace<A> discreteActionSpace;
+    protected View<A> view;
+    private int delay;
+    private int nrOfEpisodes;
+    private Method method;
+
+    public RLController(){
+    }
+
+    public void start(){
+        if(environment == null || discreteActionSpace == null || method == null){
+            throw new RuntimeException("Set environment, discreteActionSpace and method before calling .start()");
+        }
+
+        switch (method){
+            case MC_ONPOLICY_EGREEDY:
+                learning = new MonteCarloOnPolicyEGreedy<>(environment, discreteActionSpace);
+                break;
+            case TD_ONPOLICY:
+                break;
+            default:
+                throw new RuntimeException("Undefined method");
+        }
+        SwingUtilities.invokeLater(() ->{
+            view = new View<>(learning, this);
+            learning.addListener(view);
+        });
+        learning.learn(nrOfEpisodes);
+    }
+    
+    @Override
+    public void onEpsilonChange(float epsilon) {
+        learning.setEpsilon(epsilon);
+        SwingUtilities.invokeLater(() -> view.updateLearningInfoPanel());
+    }
+
+    @Override
+    public void onDelayChange(int delay) {
+    }
+
+    public RLController<A> setMethod(Method method){
+        this.method = method;
+        return this;
+    }
+    public RLController<A> setEnvironment(Environment<A> environment){
+        this.environment = environment;
+        return this;
+    }
+    @SafeVarargs
+    public final RLController<A> setAllowedActions(A... actions){
+        this.discreteActionSpace = new ListDiscreteActionSpace<>(actions);
+        return this;
+    }
+
+    public RLController<A> setDelay(int delay){
+        this.delay = delay;
+        return this;
+    }
+
+    public RLController<A> setEpisodes(int nrOfEpisodes){
+        this.nrOfEpisodes = nrOfEpisodes;
+        return this;
+    }
+
+}

src/main/java/core/controller/ViewListener.java

@@ -0,0 +1,6 @@
+package core.controller;
+
+public interface ViewListener {
+    void onEpsilonChange(float epsilon);
+    void onDelayChange(int delay);
+}

src/main/java/core/gui/LearningInfoPanel.java

@@ -0,0 +1,41 @@
+package core.gui;
+
+import core.algo.Learning;
+import core.controller.ViewListener;
+
+import javax.swing.*;
+
+public class LearningInfoPanel extends JPanel {
+    private Learning learning;
+    private JLabel policyLabel;
+    private JLabel discountLabel;
+    private JLabel epsilonLabel;
+    private JSlider epsilonSlider;
+    private JSlider delaySlider;
+
+    public LearningInfoPanel(Learning learning, ViewListener viewListener){
+        this.learning = learning;
+        setLayout(new BoxLayout(this, BoxLayout.Y_AXIS));
+        policyLabel = new JLabel();
+        discountLabel = new JLabel();
+        epsilonLabel = new JLabel();
+        epsilonSlider = new JSlider(0, 100, (int)(learning.getEpsilon() * 100));
+        epsilonSlider.addChangeListener(e -> viewListener.onEpsilonChange(epsilonSlider.getValue() / 100f));
+        add(policyLabel);
+        add(discountLabel);
+        add(epsilonLabel);
+        add(epsilonSlider);
+        refreshLabels();
+        setVisible(true);
+    }
+
+    public void refreshLabels(){
+        policyLabel.setText("Policy: " + learning.getPolicy().getClass());
+        discountLabel.setText("Discount factor: " + learning.getDiscountFactor());
+        epsilonLabel.setText("Exploration (Epsilon): " + learning.getEpsilon());
+    }
+
+    protected JSlider getEpsilonSlider(){
+        return epsilonSlider;
+    }
+}

src/main/java/core/gui/View.java

@@ -0,0 +1,102 @@
+package core.gui;
+
+import core.algo.Learning;
+import core.controller.ViewListener;
+import core.listener.LearningListener;
+import lombok.Getter;
+import org.knowm.xchart.QuickChart;
+import org.knowm.xchart.XChartPanel;
+import org.knowm.xchart.XYChart;
+
+import javax.swing.*;
+import java.awt.*;
+import java.util.ArrayList;
+import java.util.List;
+
+public class View<A extends Enum> implements LearningListener {
+    private Learning<A> learning;
+    @Getter
+    private XYChart chart;
+    @Getter
+    private LearningInfoPanel learningInfoPanel;
+    @Getter
+    private JFrame mainFrame;
+    private XChartPanel<XYChart> rewardChartPanel;
+    private ViewListener viewListener;
+    private List<Double> rewardHistory;
+
+    public View(Learning<A> learning, ViewListener viewListener){
+        this.learning = learning;
+        this.viewListener = viewListener;
+        rewardHistory = new ArrayList<>();
+        this.initMainFrame();
+    }
+
+    private void initMainFrame(){
+        mainFrame = new JFrame();
+        mainFrame.setPreferredSize(new Dimension(1280, 720));
+        mainFrame.setLayout(new BorderLayout());
+
+        initLearningInfoPanel();
+        initRewardChart();
+
+        mainFrame.add(BorderLayout.WEST, learningInfoPanel);
+        mainFrame.add(BorderLayout.CENTER, rewardChartPanel);
+
+        mainFrame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
+        mainFrame.pack();
+        mainFrame.setVisible(true);
+    }
+
+    private void initLearningInfoPanel(){
+        learningInfoPanel = new LearningInfoPanel(learning, viewListener);
+    }
+
+    private void initRewardChart(){
+        chart =
+                QuickChart.getChart(
+                        "Rewards per Episode",
+                        "Episode",
+                        "Reward",
+                        "randomWalk",
+                        new double[] {0},
+                        new double[] {0});
+        chart.getStyler().setLegendVisible(true);
+        chart.getStyler().setXAxisTicksVisible(true);
+        rewardChartPanel = new XChartPanel<>(chart);
+        rewardChartPanel.setPreferredSize(new Dimension(300,300));
+    }
+
+    public void showState(Visualizable state){
+        new JFrame(){
+            {
+                JComponent stateComponent = state.visualize();
+                setPreferredSize(new Dimension(stateComponent.getWidth(), stateComponent.getHeight()));
+                add(stateComponent);
+                setVisible(true);
+            }
+        };
+    }
+
+    public void updateRewardGraph(double recentReward){
+        rewardHistory.add(recentReward);
+        chart.updateXYSeries("randomWalk", null, rewardHistory, null);
+        rewardChartPanel.revalidate();
+        rewardChartPanel.repaint();
+    }
+
+    public void updateLearningInfoPanel(){
+        this.learningInfoPanel.refreshLabels();
+    }
+
+
+    @Override
+    public void onEpisodeEnd(double sumOfRewards) {
+        SwingUtilities.invokeLater(()->updateRewardGraph(sumOfRewards));
+    }
+
+    @Override
+    public void onEpisodeStart() {
+
+    }
+}

src/main/java/core/gui/Visualizable.java

@@ -0,0 +1,7 @@
+package core.gui;
+
+import javax.swing.*;
+
+public interface Visualizable {
+    JComponent visualize();
+}

src/main/java/core/listener/LearningListener.java

@@ -0,0 +1,6 @@
+package core.listener;
+
+public interface LearningListener{
+    void onEpisodeEnd(double sumOfRewards);
+    void onEpisodeStart();
+}

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

@@ -1,7 +1,10 @@
 package evironment.antGame;
 
 import core.State;
+import core.gui.Visualizable;
+import evironment.antGame.gui.CellColor;
 
+import javax.swing.*;
 import java.awt.*;
 import java.util.Arrays;
 
@@ -10,7 +13,7 @@ import java.util.Arrays;
  * Essentially a snapshot of the current Ant Agent
  * and therefor has to be deep copied
  */
-public class AntState implements State {
+public class AntState implements State, Visualizable {
     private final Cell[][] knownWorld;
     private final Point pos;
     private final boolean hasFood;
@@ -29,9 +32,9 @@ public class AntState implements State {
 
         int unknown = 0;
         int diff = 0;
-        for (int i = 0; i < knownWorld.length; i++) {
+        for (Cell[] cells : knownWorld) {
-            for (int j = 0; j < knownWorld[i].length; j++) {
+            for (Cell cell : cells) {
-                if(knownWorld[i][j].getType() == CellType.UNKNOWN){
+                if (cell.getType() == CellType.UNKNOWN) {
                     unknown += 1;
                 } else {
                     diff += 1;
@@ -89,4 +92,62 @@ public class AntState implements State {
         }
         return  super.equals(obj);
     }
+
+    @Override
+    public JComponent visualize() {
+        return new JScrollPane() {
+            private int cellSize;
+            private final int paneWidth = 500;
+            private final int paneHeight = 500;
+            private Font font;
+            {
+                setPreferredSize(new Dimension(paneWidth, paneHeight));
+                cellSize = (paneWidth- knownWorld.length) /knownWorld.length;
+                font = new Font("plain", Font.BOLD, cellSize);
+                JPanel worldPanel = new JPanel(){
+                    {
+                        setPreferredSize(new Dimension(knownWorld.length * cellSize, knownWorld[0].length * cellSize));
+                        setVisible(true);
+
+                        addMouseWheelListener(e -> {
+                            if(e.getWheelRotation() > 0){
+                                cellSize -= 1;
+                            }else {
+                                cellSize += 1;
+                            }
+                            font = new Font("plain", Font.BOLD, cellSize);
+                            setPreferredSize(new Dimension(knownWorld.length * cellSize, knownWorld[0].length * cellSize));
+                            revalidate();
+                            repaint();
+                        });
+                    }
+
+                    @Override
+                    public void paintComponent(Graphics g) {
+                        super.paintComponent(g);
+                        for (int i = 0; i < knownWorld.length; i++) {
+                            for (int j = 0; j < knownWorld[0].length; j++) {
+                                g.setColor(Color.BLACK);
+                                g.drawRect(i*cellSize, j*cellSize, cellSize, cellSize);
+                                g.setColor(CellColor.map.get(knownWorld[i][j].getType()));
+                                if(knownWorld[i][j].getFood() > 0){
+                                    g.setColor(Color.YELLOW);
+                                }
+                                g.fillRect(i*cellSize+1, j*cellSize+1, cellSize -1, cellSize-1);
+                            }
+                        }
+                        if(hasFocus()){
+                            g.setColor(Color.RED);
+                        }else {
+                            g.setColor(Color.BLACK);
+                        }
+                        g.setFont(font);
+                        g.drawString("A", pos.x * cellSize, (pos.y + 1) * cellSize);
+                    }
+                };
+                getViewport().add(worldPanel);
+                setVisible(true);
+            }
+        };
+    }
 }

src/main/java/evironment/antGame/AntWorld.java

@@ -35,21 +35,15 @@ public class AntWorld implements Environment<AntAction>{
 
     private int tick;
     private int maxEpisodeTicks;
-    MainFrame gui;
 
     public AntWorld(int width, int height, double foodDensity){
         grid = new Grid(width, height, foodDensity);
         antAgent = new AntAgent(width, height);
         myAnt = new Ant();
-        gui = new MainFrame(this, antAgent);
         maxEpisodeTicks = 1000;
         reset();
     }
 
-    public MainFrame getGui(){
-        return gui;
-    }
-
     public AntWorld(){
         this(Constants.DEFAULT_GRID_WIDTH, Constants.DEFAULT_GRID_HEIGHT, Constants.DEFAULT_FOOD_DENSITY);
     }
@@ -166,7 +160,6 @@ public class AntWorld implements Environment<AntAction>{
 
 
         StepResultEnvironment result = new StepResultEnvironment(newState, reward, done, info);
-        getGui().update(action, result);
         return result;
     }
 
@@ -216,6 +209,6 @@ public class AntWorld implements Environment<AntAction>{
                 new AntWorld(3, 3, 0.1),
                 new ListDiscreteActionSpace<>(AntAction.values())
         );
-        monteCarlo.learn(20000,5);
+        monteCarlo.learn(20000);
     }
 }

src/main/java/example/RunningAnt.java

@@ -0,0 +1,21 @@
+package example;
+
+import core.RNG;
+import core.algo.Method;
+import core.controller.RLController;
+import evironment.antGame.AntAction;
+import evironment.antGame.AntWorld;
+
+public class RunningAnt {
+    public static void main(String[] args) {
+        RNG.setSeed(1234);
+
+        RLController<AntAction> rl = new RLController<AntAction>()
+                            .setEnvironment(new AntWorld(3,3,0.1))
+                            .setAllowedActions(AntAction.values())
+                            .setMethod(Method.MC_ONPOLICY_EGREEDY)
+                            .setDelay(10)
+                            .setEpisodes(1000);
+        rl.start();
+    }
+}

src/main/java/example/Test.java

@@ -0,0 +1,52 @@
+package example;
+
+public class Test {
+    interface Drawable{
+        void draw();
+    }
+    interface State{
+        int getInt();
+    }
+
+    static class A implements  Drawable, State{
+        private int k;
+        public A(int a){
+            k = a;
+        }
+        @Override
+        public void draw() {
+            System.out.println("draw " + k);
+        }
+
+        @Override
+        public int getInt() {
+            System.out.println("getInt" + k);
+            return k;
+        }
+    }
+
+    static class B  implements State{
+        @Override
+        public int getInt() {
+            return 0;
+        }
+    }
+
+    public static void main(String[] args) {
+        State state = new A(24);
+        State state2 = new B();
+        state.getInt();
+
+        System.out.println(state2 instanceof Drawable);
+        drawState(state2);
+    }
+
+    static void drawState(State s){
+        if(s instanceof Drawable){
+            Drawable d = (Drawable) s;
+            d.draw();
+        }else{
+            System.out.println("invalid");
+        }
+    }
+}