spawn start of antgame constant

# Conflicts:
#	OptimalityDifferentDiscountFactors.R
#	src/main/java/core/algo/td/QLearningOffPolicyTDControl.java
#	src/main/java/example/ContinuousAnt.java

rawResults/convergenceAdvancedMC.txt

@@ -17,5 +17,5 @@
 0.8,19870,3288,13724,4492,8159,5058,16764,5648,9462,19071,3914,1242,8262,26004,4036,9421,4914,2535,5362,7298,9587,37133,1837,35325,15272,14922,14138,7115,17236,5123,12157,37380,6086,37390,1672,15573,14241,2049,2602,6802,22362,7936,7544,5330,13155,16016,4544,1489,3780,6326,7794,31553,2808,1493,7788,12646,30464,22312,1681,12084,4163,2197,7950,22478,5106,26771,4382,10615,2586,12214,4799,6297,7589,4585,30365,32302,15734,5480,8626,7387,11932,4245,21532,1710,12737,7132,4740,14578,10680,8266,17300,4213,3264,35920,38026,10272,3984,2279,9739,33900
 0.85,5493,10568,19366,5705,15430,8183,5721,13314,36667,33059,3753,40243,23888,25085,21843,6856,2803,9434,4794,29944,10730,39271,4484,23990,6350,16180,8099,4298,11220,4624,5946,24895,8464,4416,6619,2800,4081,12459,1981,12488,6380,9597,10328,1901,24563,13059,3639,12988,2604,4440,22666,1775,4078,5175,1144,3759,11119,1856,34970,10831,2229,5333,17121,9698,14919,2353,3963,8189,36145,13920,5301,16516,2446,46848,3985,,20640,151501,17556,1882,44216,39795,1638,57957,62050,3130,3693,5563,9780,3327,22969,39357,13749,37555,60070,9249,35426,4405,8340,18973
 0.9,27355,24592,18962,2318,17604,35725,14327,38167,25602,50236,4999,9023,5562,7541,11799,25139,8724,12642,28509,57095,2147,5909,5414,12572,10018,68830,45393,18962,51656,25601,3444,45667,16813,57110,16492,3991,7315,17775,69277,34769,29824,11087,26371,3479,2540,9597,32593,13169,8588,2794,40136,56004,65307,24864,35523,19491,2673,5363,4799,5852,28566,42427,44011,40146,3757,1115,49574,5798,24249,2576,118943,6169,65584,7057,49505,116138,52083,1809,127776,3214,25689,103442,15260,62754,12390,3233,35309,68989,6615,30593,2503,29359,98237,11900,3240,64969,84134,25361,7384,13141
-0.95,24269,14543,6828,3800,41079,47279,27177,17286,9802,7114,3756,85275,14507,34993,15139,15184,90742,27554,23713,6453,15157,7045,8048,47550,84540,93729,68601,6274,4713,30578,5024,94239,7315,8193,46871,96466,3695,70915,62947,32258,66228,2114,5084,12686,62905,19158,20940,36270,9037,34034,15016,15530,46276,11063,8586,15635,7196,70708,50836,22464,13463,86986,43541,2001,40565,28534,44700,5625,6552,16140,2450,8492,3304,22904,20951,100472,131147,131728,43674,514,79827,181148,31431,4761,1515,2075,138139,137795,71014170145,60000,42790,179835,18982,48085,28398,56788,126115,5442,118289,9386
+0.95,24269,14543,6828,3800,41079,47279,27177,17286,9802,7114,3756,85275,14507,34993,15139,15184,90742,27554,23713,6453,15157,7045,8048,47550,84540,93729,68601,6274,4713,30578,5024,94239,7315,8193,46871,96466,3695,70915,62947,32258,66228,2114,5084,12686,62905,19158,20940,36270,9037,34034,15016,15530,46276,11063,8586,15635,7196,70708,50836,22464,13463,86986,43541,2001,40565,28534,44700,5625,6552,16140,2450,8492,3304,22904,20951,100472,131147,131728,43674,514,79827,181148,31431,4761,1515,2075,138139,137795,71014,170145,60000,42790,179835,18982,48085,28398,56788,126115,5442,118289
 1.0,11364,6363,8012,109822,19730,8425,21388,7864,18427,34072,3126,52381,35105,86487,73913,88033,76264,105864,30103,9522,31049,3180,4838,4078,133687,39236,59239,22968,21540,98395,109063,4050,5612,4990,9933,83766,140114,116077,135653,130826,130070,92207,14994,87801,1577,70868,133816,79790,1587,23322,22071,13903,3584,9721,,38605,52375,67392,10075,97733,46173,29647,2558,28151,162569,4054,10537,30871,45538,97835,45132,35042,70203,3862,100614,84525,140691,81880,80914,35187,11596,51448,2945,56551,39236,84707,64324,100588,78645,12929,32701,63306,163991,2864,34802,72929,198161,71332,98627,137754

src/main/java/core/RNG.java

@@ -17,24 +17,30 @@ public class RNG {
     private static Random rng;
     private static Random rngEnv;
     private static int seed = 123;
+    private static int envSeed = 13;
     static {
         rng = new Random();
+        rngEnv = new Random();
         setSeed(seed, true);
     }
 
     public static Random getRandom() {
         return rng;
     }
-
+    public static Random getRandomEnv() {
-    public static Random getEnvRandom() {
         return rngEnv;
     }
 
-    public static void setSeed(int seed, boolean setEnvSeed) {
+    public static void setSeed(int seed, boolean setEnvRandom) {
         RNG.seed = seed;
         rng.setSeed(seed);
-        if(setEnvSeed) {
+        if(setEnvRandom) {
-            rngEnv.setSeed(seed);
+            rngEnv.setSeed(13);
         }
     }
+
+    public static void setSeed(int seed) {
+        setSeed(seed, true);
+    }
+
 }

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

@@ -5,17 +5,12 @@ import core.Environment;
 import core.LearningConfig;
 import core.StepResult;
 import core.listener.LearningListener;
-import example.DinoSampling;
 import lombok.Getter;
 import lombok.Setter;
 
-import java.io.File;
 import java.io.IOException;
 import java.io.ObjectInputStream;
 import java.io.ObjectOutputStream;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.StandardOpenOption;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.atomic.AtomicInteger;
@@ -91,20 +86,6 @@ public abstract class EpisodicLearning<A extends Enum> extends Learning<A> imple
         super.dispatchStepEnd();
         timestamp++;
         timestampCurrentEpisode++;
-        // TODO: more sophisticated way to check convergence
-        if(timestampCurrentEpisode > 50000) {
-            converged = true;
-            // t
-            File file = new File(DinoSampling.FILE_NAME);
-            try {
-                Files.writeString(Path.of(file.getPath()),  currentEpisode/2 + ",", StandardOpenOption.APPEND);
-            } catch (IOException e) {
-                e.printStackTrace();
-            }
-            System.out.println("converged after: " + currentEpisode/2 + " episode!");
-            episodesToLearn.set(0);
-            dispatchLearningEnd();
-        }
     }
 
     @Override

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

@@ -16,8 +16,6 @@ import java.util.HashSet;
 import java.util.List;
 import java.util.Set;
 import java.util.concurrent.CopyOnWriteArrayList;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Executors;
 
 /**
  *
@@ -99,7 +97,7 @@ public abstract class Learning<A extends Enum>{
 
     public void save(ObjectOutputStream oos) throws IOException {
         oos.writeObject(rewardHistory);
-        oos.writeObject(stateActionTable);
+        // oos.writeObject(stateActionTable);
     }
 
     public void load(ObjectInputStream ois) throws IOException, ClassNotFoundException {

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

@@ -3,8 +3,6 @@ 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;
 
@@ -14,7 +12,7 @@ import java.io.ObjectOutputStream;
 import java.util.*;
 
 /**
- * Includes both variants of Monte-Carlo methods
+ * Includes both! variants of Monte-Carlo methods
  * Default method is First-Visit.
  * Change to Every-Visit by setting flag "useEveryVisit" in the constructor to true.
  * @param <A>
@@ -25,17 +23,10 @@ public class MonteCarloControlEGreedy<A extends Enum> extends EpisodicLearning<A
     private Map<Pair<State, A>, Integer> returnCount;
     private boolean isEveryVisit;
 
-    // t
-    private float epsilon;
-    // t
-    private Policy<A> greedyPolicy = new GreedyPolicy<>();
-
 
     public MonteCarloControlEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay, boolean useEveryVisit) {
         super(environment, actionSpace, discountFactor, delay);
         isEveryVisit = useEveryVisit;
-        // t
-        this.epsilon = epsilon;
         this.policy = new EpsilonGreedyPolicy<>(epsilon);
         this.stateActionTable = new DeterministicStateActionTable<>(this.actionSpace);
         returnSum = new HashMap<>();
@@ -64,12 +55,7 @@ public class MonteCarloControlEGreedy<A extends Enum> extends EpisodicLearning<A
 
         while(envResult == null || !envResult.isDone()) {
             Map<A, Double> actionValues = stateActionTable.getActionValues(state);
-            A chosenAction;
+            A  chosenAction = policy.chooseAction(actionValues);
-            if(currentEpisode % 2 == 1){
-                chosenAction = greedyPolicy.chooseAction(actionValues);
-            }else{
-                chosenAction = policy.chooseAction(actionValues);
-            }
 
             envResult = environment.step(chosenAction);
             State nextState = envResult.getState();
@@ -86,12 +72,9 @@ public class MonteCarloControlEGreedy<A extends Enum> extends EpisodicLearning<A
             }
             timestamp++;
             dispatchStepEnd();
-            if(converged) return;
         }
 
-        if(currentEpisode % 2 == 1){
+
-            return;
-        }
 
         //  System.out.printf("Episode %d \t Reward: %f \n", currentEpisode, sumOfRewards);
         HashMap<Pair<State, A>, List<Integer>> stateActionPairs = new LinkedHashMap<>();

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

@@ -5,7 +5,14 @@ import core.algo.EpisodicLearning;
 import core.policy.EpsilonGreedyPolicy;
 import core.policy.GreedyPolicy;
 import core.policy.Policy;
+import evironment.antGame.Reward;
+import example.ContinuousAnt;
 
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.StandardOpenOption;
 import java.util.Map;
 
 public class QLearningOffPolicyTDControl<A extends Enum> extends EpisodicLearning<A> {
@@ -37,25 +44,57 @@ public class QLearningOffPolicyTDControl<A extends Enum> extends EpisodicLearnin
 
 
         sumOfRewards = 0;
+        int timestampTilFood = 0;
+        int foodCollected = 0;
+        int foodTimestampsTotal= 0;
         while(envResult == null || !envResult.isDone()) {
             actionValues = stateActionTable.getActionValues(state);
-            A action;
+            A action = policy.chooseAction(actionValues);
-            if(currentEpisode % 2 == 0) {
+
-                action = greedyPolicy.chooseAction(actionValues);
-            } else {
-                action = policy.chooseAction(actionValues);
-            }
-            if(converged) return;
             // Take a step
             envResult = environment.step(action);
             double reward = envResult.getReward();
             State nextState = envResult.getState();
             sumOfRewards += reward;
-            if(currentEpisode % 2 == 0) {
+            timestampTilFood++;
-                state = nextState;
+
-                dispatchStepEnd();
+            if(reward == Reward.FOOD_DROP_DOWN_SUCCESS) {
-                continue;
+                foodCollected++;
+                foodTimestampsTotal += timestampTilFood;
+                File file = new File(ContinuousAnt.FILE_NAME);
+                if(foodCollected % 1000 == 0) {
+                    System.out.println(foodTimestampsTotal / 1000f + " " + timestampCurrentEpisode);
+                    try {
+                        Files.writeString(Path.of(file.getPath()), foodTimestampsTotal / 1000f + ",", StandardOpenOption.APPEND);
+                    } catch (IOException e) {
+                        e.printStackTrace();
                     }
+                    foodTimestampsTotal = 0;
+                }
+                if(foodCollected == 1000){
+                    ((EpsilonGreedyPolicy<A>) this.policy).setEpsilon(0.15f);
+                }
+                if(foodCollected == 2000){
+                    ((EpsilonGreedyPolicy<A>) this.policy).setEpsilon(0.10f);
+                }
+                if(foodCollected == 3000){
+                    ((EpsilonGreedyPolicy<A>) this.policy).setEpsilon(0.05f);
+                }
+                if(foodCollected == 4000){
+                    System.out.println("Reached 0 exploration");
+                    ((EpsilonGreedyPolicy<A>) this.policy).setEpsilon(0.00f);
+                }
+                if(foodCollected == 15000){
+                    try {
+                        Files.writeString(Path.of(file.getPath()),  "\n", StandardOpenOption.APPEND);
+                    } catch (IOException e) {
+                        e.printStackTrace();
+                    }
+                    return;
+                }
+                timestampTilFood = 0;
+            }
+
             // Q Update
             double currentQValue = stateActionTable.getActionValues(state).get(action);
             // maxQ(S', a);

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

@@ -11,7 +11,6 @@ import java.util.Map;
 
 public class SARSA<A extends Enum> extends EpisodicLearning<A> {
     private float alpha;
-    private Policy<A> greedyPolicy = new GreedyPolicy<>();
 
     public SARSA(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, float learningRate, int delay) {
         super(environment, actionSpace, discountFactor, delay);
@@ -35,18 +34,13 @@ public class SARSA<A extends Enum> extends EpisodicLearning<A> {
 
         StepResultEnvironment envResult = null;
         Map<A, Double> actionValues = stateActionTable.getActionValues(state);
-        A action;
+        A  action = policy.chooseAction(actionValues);
-        if(currentEpisode % 2 == 1){
+
-            action = greedyPolicy.chooseAction(actionValues);
-        }else{
-            action = policy.chooseAction(actionValues);
-        }
         //A action = policy.chooseAction(actionValues);
 
         sumOfRewards = 0;
         while(envResult == null || !envResult.isDone()) {
 
-            if(converged) return;
             // Take a step
             envResult = environment.step(action);
             sumOfRewards += envResult.getReward();
@@ -56,19 +50,8 @@ public class SARSA<A extends Enum> extends EpisodicLearning<A> {
             // Pick next action
             actionValues = stateActionTable.getActionValues(nextState);
 
-            A nextAction;
+            A  nextAction = policy.chooseAction(actionValues);
-            if(currentEpisode % 2 == 1){
+
-                nextAction = greedyPolicy.chooseAction(actionValues);
-            }else{
-                nextAction = policy.chooseAction(actionValues);
-            }
-            //A nextAction = policy.chooseAction(actionValues);
-            if(currentEpisode % 2 == 1){
-                state = nextState;
-                action = nextAction;
-                dispatchStepEnd();
-                continue;
-            }
             // td update
             // target = reward + gamma * Q(nextState, nextAction)
             double currentQValue = stateActionTable.getActionValues(state).get(action);

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

@@ -54,7 +54,7 @@ public class AntWorld implements Environment<AntAction>, Visualizable {
 
     protected StepCalculation processStep(AntAction action) {
         StepCalculation sc = new StepCalculation();
-        sc.reward = -1;
+        sc.reward = Reward.DEFAULT_REWARD;
         sc.info = "";
         sc.done = false;
         Cell currentCell = grid.getCell(myAnt.getPos());
@@ -84,10 +84,10 @@ public class AntWorld implements Environment<AntAction>, Visualizable {
             case PICK_UP:
                 if(myAnt.hasFood()) {
                     // Ant tries to pick up food but can only hold one piece
-                    sc.reward += Reward.FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY;
+                    sc.reward = Reward.FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY;
                 } else if(currentCell.getFood() == 0) {
                     // Ant tries to pick up food on cell that has no food on it
-                    sc.reward += Reward.FOOD_PICK_UP_FAIL_NO_FOOD;
+                    sc.reward = Reward.FOOD_PICK_UP_FAIL_NO_FOOD;
                 } else if(currentCell.getFood() > 0) {
                     // Ant successfully picks up food
                     currentCell.setFood(currentCell.getFood() - 1);
@@ -98,13 +98,13 @@ public class AntWorld implements Environment<AntAction>, Visualizable {
             case DROP_DOWN:
                 if(!myAnt.hasFood()) {
                     // Ant had no food to drop
-                    sc.reward += Reward.FOOD_DROP_DOWN_FAIL_NO_FOOD;
+                    sc.reward = Reward.FOOD_DROP_DOWN_FAIL_NO_FOOD;
                 } else {
                     myAnt.setHasFood(false);
                     // negative reward if the agent drops food on any other field
                     // than the starting point
                     if(currentCell.getType() != CellType.START) {
-                        sc.reward += Reward.FOOD_DROP_DOWN_FAIL_NOT_START;
+                        sc.reward = Reward.FOOD_DROP_DOWN_FAIL_NOT_START;
                         // Drop food onto the ground
                         currentCell.setFood(currentCell.getFood() + 1);
                     } else {
@@ -122,10 +122,10 @@ public class AntWorld implements Environment<AntAction>, Visualizable {
         if(!sc.stayOnCell) {
             if(!isInGrid(sc.potentialNextPos)) {
                 sc.stayOnCell = true;
-                sc.reward += Reward.RAN_INTO_WALL;
+                sc.reward = Reward.RAN_INTO_WALL;
             } else if(hitObstacle(sc.potentialNextPos)) {
                 sc.stayOnCell = true;
-                sc.reward += Reward.RAN_INTO_OBSTACLE;
+                sc.reward = Reward.RAN_INTO_OBSTACLE;
             }
         }
 

src/main/java/evironment/antGame/Grid.java

@@ -60,8 +60,8 @@ public class Grid {
         Point potFood = new Point(0, 0);
         CellType potFieldType;
         while(!foodSpawned) {
-            potFood.x = RNG.getEnvRandom().nextInt(width);
+            potFood.x = RNG.getRandomEnv().nextInt(width);
-            potFood.y = RNG.getEnvRandom().nextInt(height);
+            potFood.y = RNG.getRandomEnv().nextInt(height);
             potFieldType = grid[potFood.x][potFood.y].getType();
             if(potFieldType != CellType.START && grid[potFood.x][potFood.y].getFood() == 0 && potFieldType != CellType.OBSTACLE) {
                 grid[potFood.x][potFood.y].setFood(1);

src/main/java/evironment/antGame/Reward.java

@@ -1,16 +1,17 @@
 package evironment.antGame;
 
 public class Reward {
-    public static final double FOOD_PICK_UP_SUCCESS = 1;
+    public static final double DEFAULT_REWARD = -1;
-    public static final double FOOD_PICK_UP_FAIL_NO_FOOD = -1;
+    public static final double FOOD_PICK_UP_SUCCESS = 0;
-    public static final double FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY = -1;
+    public static final double FOOD_PICK_UP_FAIL_NO_FOOD = -2;
+    public static final double FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY = -2;
 
-    public static final double FOOD_DROP_DOWN_FAIL_NO_FOOD = -1;
+    public static final double FOOD_DROP_DOWN_FAIL_NO_FOOD = -2;
-    public static final double FOOD_DROP_DOWN_FAIL_NOT_START = -1;
+    public static final double FOOD_DROP_DOWN_FAIL_NOT_START = -2;
-    public static final double FOOD_DROP_DOWN_SUCCESS = 40;
+    public static final double FOOD_DROP_DOWN_SUCCESS = 1;
 
     public static final double UNKNOWN_FIELD_EXPLORED = 0;
 
-    public static final double RAN_INTO_WALL = -1;
+    public static final double RAN_INTO_WALL = -2;
-    public static final double RAN_INTO_OBSTACLE = -1;
+    public static final double RAN_INTO_OBSTACLE = -2;
 }

src/main/java/evironment/blackjack/cards/CardDeck.java

@@ -29,6 +29,6 @@ public class CardDeck {
             nextInt(int bound) returns random int value from (inclusive) 0
             and EXCLUSIVE! bound
           */
-        return cards.get(RNG.getEnvRandom().nextInt(cards.size()));
+        return cards.get(RNG.getRandomEnv().nextInt(cards.size()));
     }
 }

src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java

@@ -31,7 +31,7 @@ public class DinoWorldAdvanced extends DinoWorld{
     protected void spawnNewObstacle() {
         int dx;
         int xSpawn;
-        double ran = RNG.getEnvRandom().nextDouble();
+        double ran = RNG.getRandomEnv().nextDouble();
         if(ran < 0.25){
             dx = -(int) (0.35 * Config.OBSTACLE_SPEED);
         }else if(ran < 0.5){
@@ -41,7 +41,7 @@ public class DinoWorldAdvanced extends DinoWorld{
         } else{
             dx = -(int) (3.5 * Config.OBSTACLE_SPEED);
         }
-        double ran2 = RNG.getEnvRandom().nextDouble();
+        double ran2 = RNG.getRandomEnv().nextDouble();
         if(ran2 < 0.25) {
             // randomly spawning more right outside of the screen
             xSpawn = Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE;

src/main/java/example/BlackJack.java

@@ -9,7 +9,7 @@ import evironment.blackjack.PlayerAction;
 
 public class BlackJack {
     public static void main(String[] args) {
-        RNG.setSeed(55, true);
+        RNG.setSeed(55);
 
         RLController<PlayerAction> rl = new RLControllerGUI<>(
                 new BlackJackTable(),

src/main/java/example/ContinuousAnt.java

@@ -0,0 +1,37 @@
+package example;
+
+import core.RNG;
+import core.algo.Method;
+import core.controller.RLController;
+import core.controller.RLControllerGUI;
+import evironment.antGame.AntAction;
+import evironment.antGame.AntWorldContinuous;
+
+import java.io.File;
+import java.io.IOException;
+
+public class ContinuousAnt {
+    public static final String FILE_NAME = "converge.txt";
+
+    public static void main(String[] args) {
+        File file = new File(FILE_NAME);
+        try {
+            file.createNewFile();
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+        RNG.setSeed(13, true);
+        RLController<AntAction> rl = new RLControllerGUI<>(
+                new AntWorldContinuous(8, 8),
+                Method.Q_LEARNING_OFF_POLICY_CONTROL,
+                AntAction.values());
+        rl.setDelay(20);
+        rl.setNrOfEpisodes(1);
+        // 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99
+        rl.setDiscountFactor(0.05f);
+        // 0.1, 0.3, 0.5, 0.7 0.9
+        rl.setLearningRate(0.9f);
+        rl.setEpsilon(0.2f);
+        rl.start();
+    }
+}

src/main/java/example/DinoSampling.java

@@ -1,52 +0,0 @@
-package example;
-
-import core.RNG;
-import core.algo.Method;
-import core.controller.RLController;
-import evironment.jumpingDino.DinoAction;
-import evironment.jumpingDino.DinoWorldAdvanced;
-
-import java.io.File;
-import java.io.IOException;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.StandardOpenOption;
-
-public class DinoSampling {
-    public static final String FILE_NAME = "advancedEveryVisit.txt";
-    public static void main(String[] args) {
-        File file = new File(FILE_NAME);
-        try {
-            file.createNewFile();
-        } catch (IOException e) {
-            e.printStackTrace();
-        }
-        for(float f = 0.05f; f <= 1.003; f += 0.05f) {
-            try {
-                Files.writeString(Path.of(file.getPath()), f + ",", StandardOpenOption.APPEND);
-            } catch (IOException e) {
-                e.printStackTrace();
-            }
-            for(int i = 1; i <= 100; i++) {
-                System.out.println("seed: " + i * 13);
-                RNG.setSeed(i * 13, true);
-
-                RLController<DinoAction> rl = new RLController<>(
-                        new DinoWorldAdvanced(),
-                        Method.MC_CONTROL_EVERY_VISIT,
-                        DinoAction.values());
-                rl.setDelay(0);
-                rl.setDiscountFactor(1f);
-                rl.setEpsilon(f);
-                rl.setLearningRate(1f);
-                rl.setNrOfEpisodes(400000);
-                rl.start();
-            }
-            try {
-                Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND);
-            } catch (IOException e) {
-                e.printStackTrace();
-            }
-        }
-    }
-}

src/main/java/example/JumpingDino.java

@@ -4,11 +4,12 @@ import core.RNG;
 import core.algo.Method;
 import core.controller.RLController;
 import evironment.jumpingDino.DinoAction;
+import evironment.jumpingDino.DinoWorld;
 import evironment.jumpingDino.DinoWorldAdvanced;
 
 public class JumpingDino {
     public static void main(String[] args) {
-        RNG.setSeed(29, true);
+        RNG.setSeed(29);
 
         RLController<DinoAction> rl = new RLController<>(
                 new DinoWorldAdvanced(),
@@ -16,9 +17,9 @@ public class JumpingDino {
                 DinoAction.values());
 
         rl.setDelay(0);
-        rl.setDiscountFactor(1f);
+        rl.setDiscountFactor(9f);
         rl.setEpsilon(0.05f);
-        rl.setLearningRate(1f);
+        rl.setLearningRate(0.8f);
         rl.setNrOfEpisodes(100000);
         rl.start();
     }

src/main/java/example/RunningAnt.java

@@ -9,7 +9,7 @@ import evironment.antGame.AntWorld;
 
 public class RunningAnt {
     public static void main(String[] args) {
-        RNG.setSeed(56, true);
+        RNG.setSeed(56);
 
         RLController<AntAction> rl = new RLControllerGUI<>(
                 new AntWorld(8, 8),
@@ -19,6 +19,7 @@ public class RunningAnt {
         rl.setDelay(200);
         rl.setNrOfEpisodes(10000);
         rl.setDiscountFactor(0.9f);
+        rl.setLearningRate(0.9f);
         rl.setEpsilon(0.15f);
         rl.start();
     }