From 9b54b72a2537a3225700e11c74a84e5bed1b2b6e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Tue, 3 Mar 2020 02:52:39 +0100 Subject: [PATCH 01/18] add epsilon convergence test and will remove unnecessary multithreaded learning --- Rplot.png | Bin 0 -> 7659 bytes convergence.txt | 20 +++++ src/main/java/core/algo/EpisodicLearning.java | 78 +++++++++++------- src/main/java/core/algo/Learning.java | 6 +- .../MonteCarloControlFirstVisitEGreedy.java | 36 ++++++-- .../java/core/controller/RLController.java | 28 ++++--- src/main/java/example/JumpingDino.java | 48 ++++++++--- src/main/java/example/Results | 15 ++++ 8 files changed, 167 insertions(+), 64 deletions(-) create mode 100644 Rplot.png create mode 100644 convergence.txt create mode 100644 src/main/java/example/Results diff --git a/Rplot.png b/Rplot.png new file mode 100644 index 0000000000000000000000000000000000000000..dc1e2e49536ed7d5a1f8ebcfca38b02fedbfc870 GIT binary patch literal 7659 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zY4-u0_JS?WK3qz%uwGko(((W>NojDR!eg+_?ozI{Hev_UHCmY3I!dqlvPcbMwA$D$ zOa~>T8+?jQ?YvIE{qAQrosr&k5kQvCLTgA&JOx}{^A!YDUr=*ld36E)XKtduz?lX1 ziB%igjGz1bP-fI-#~ZPgZ2D-HU098zTanYZ;{BK$nmIFMkort4R^fTI5@$M#d!ImeQGdsq$cgB4kp5NTiVz zlZfp!_k}oqNLE;-U1_tq?hi^2t8IHn6d&_xQx|r8LvWv?Ns=U`bV!o;eWkw*J!LWn z;L}`tMp)kZWK$7kTOO{LAlT+>XPTHM<9IbD{i#A_1GlhC=&8duATX;xBTc}Lm=n=o{V z#OEZo0Tasecm388b$t08(U!|wr|>4x3;9Kz5!%I1e!PvU}Dqo extends Learning imple protected int episodeSumCurrentSecond; protected double sumOfRewards; protected List> episode = new ArrayList<>(); - + protected int timestampCurrentEpisode = 0; + protected boolean converged; public EpisodicLearning(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, int delay) { super(environment, actionSpace, discountFactor, delay); initBenchMarking(); @@ -50,7 +55,7 @@ public abstract class EpisodicLearning extends Learning imple private void initBenchMarking(){ new Thread(()->{ - while (true){ + while (currentlyLearning){ episodePerSecond = episodeSumCurrentSecond; episodeSumCurrentSecond = 0; try { @@ -62,7 +67,7 @@ public abstract class EpisodicLearning extends Learning imple }).start(); } - protected void dispatchEpisodeEnd(){ + private void dispatchEpisodeEnd(){ ++episodeSumCurrentSecond; if(rewardHistory.size() > 10000){ rewardHistory.clear(); @@ -73,20 +78,20 @@ public abstract class EpisodicLearning extends Learning imple } } - protected void dispatchEpisodeStart(){ + private void dispatchEpisodeStart(){ ++currentEpisode; /* - 2f 0.02 => 100 - 1.5f 0.02 => 75 - 1.4f 0.02 => fail - 1.5f 0.1 => 16 ! - */ - if(this.policy instanceof EpsilonGreedyPolicy){ - float ep = 1.5f/(float)currentEpisode; - if(ep < 0.10) ep = 0; - ((EpsilonGreedyPolicy) this.policy).setEpsilon(ep); - System.out.println(ep); - } + 2f 0.02 => 100 + 1.5f 0.02 => 75 + 1.4f 0.02 => fail + 1.5f 0.1 => 16 ! + */ +// if(this.policy instanceof EpsilonGreedyPolicy){ +// float ep = 2f/(float)currentEpisode; +// if(ep < 0.02) ep = 0; +// ((EpsilonGreedyPolicy) this.policy).setEpsilon(ep); +// System.out.println(ep); +// } episodesToLearn.decrementAndGet(); for(LearningListener l: learningListeners){ l.onEpisodeStart(); @@ -97,10 +102,20 @@ public abstract class EpisodicLearning extends Learning imple protected void dispatchStepEnd() { super.dispatchStepEnd(); timestamp++; + timestampCurrentEpisode++; // TODO: more sophisticated way to check convergence - if(timestamp > 300000){ - System.out.println("converged after: " + currentEpisode + " episode!"); - interruptLearning(); + if(timestampCurrentEpisode > 300000){ + converged = true; + // t + File file = new File("convergence.txt"); + 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(); } } @@ -110,20 +125,23 @@ public abstract class EpisodicLearning extends Learning imple } private void startLearning(){ - learningExecutor.submit(()->{ - dispatchLearningStart(); - while(episodesToLearn.get() > 0){ - dispatchEpisodeStart(); - nextEpisode(); - dispatchEpisodeEnd(); - } - synchronized (this){ - dispatchLearningEnd(); - notifyAll(); - } - }); + dispatchLearningStart(); + while(episodesToLearn.get() > 0){ + + dispatchEpisodeStart(); + timestampCurrentEpisode = 0; + nextEpisode(); + dispatchEpisodeEnd(); + } + synchronized (this){ + dispatchLearningEnd(); + notifyAll(); + } } + public void learnMoreEpisodes(int nrOfEpisodes){ + episodesToLearn.addAndGet(nrOfEpisodes); + } /** * Stopping the while loop by setting episodesToLearn to 0. * The current episode can not be interrupted, so the sleep delay diff --git a/src/main/java/core/algo/Learning.java b/src/main/java/core/algo/Learning.java index fbba9ef..1bb5207 100644 --- a/src/main/java/core/algo/Learning.java +++ b/src/main/java/core/algo/Learning.java @@ -42,8 +42,7 @@ public abstract class Learning{ @Setter protected int delay; protected List rewardHistory; - protected ExecutorService learningExecutor; - protected boolean currentlyLearning; + protected volatile boolean currentlyLearning; public Learning(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, int delay) { this.environment = environment; @@ -53,7 +52,6 @@ public abstract class Learning{ currentlyLearning = false; learningListeners = new HashSet<>(); rewardHistory = new CopyOnWriteArrayList<>(); - learningExecutor = Executors.newSingleThreadExecutor(); } public Learning(Environment environment, DiscreteActionSpace actionSpace, float discountFactor) { @@ -89,8 +87,6 @@ public abstract class Learning{ protected void dispatchLearningEnd() { currentlyLearning = false; - System.out.println("Checksum: " + checkSum); - System.out.println("Reward Checksum: " + rewardCheckSum); for (LearningListener l : learningListeners) { l.onLearningEnd(); } diff --git a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java b/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java index 50e9ce2..5bc93d3 100644 --- a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java +++ b/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java @@ -3,12 +3,17 @@ 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.IOException; -import java.io.ObjectInputStream; -import java.io.ObjectOutputStream; +import java.io.*; +import java.net.URI; +import java.nio.file.Files; +import java.nio.file.Path; +import java.nio.file.Paths; +import java.nio.file.StandardOpenOption; import java.util.*; /** @@ -35,8 +40,16 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic private Map, Double> returnSum; private Map, Integer> returnCount; + // t + private float epsilon; + // t + private Policy greedyPolicy = new GreedyPolicy<>(); + + public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay) { super(environment, actionSpace, discountFactor, delay); + // t + this.epsilon = epsilon; this.policy = new EpsilonGreedyPolicy<>(epsilon); this.stateActionTable = new DeterministicStateActionTable<>(this.actionSpace); returnSum = new HashMap<>(); @@ -58,12 +71,16 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic } sumOfRewards = 0; StepResultEnvironment envResult = null; - //TODO extract to learning - int timestamp = 0; + while(envResult == null || !envResult.isDone()) { Map actionValues = stateActionTable.getActionValues(state); - A chosenAction = policy.chooseAction(actionValues); - checkSum += chosenAction.ordinal(); + A chosenAction; + if(currentEpisode % 2 == 1){ + chosenAction = greedyPolicy.chooseAction(actionValues); + }else{ + chosenAction = policy.chooseAction(actionValues); + } + envResult = environment.step(chosenAction); State nextState = envResult.getState(); sumOfRewards += envResult.getReward(); @@ -79,6 +96,11 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic } timestamp++; dispatchStepEnd(); + if(converged) return; + } + + if(currentEpisode % 2 == 1){ + return; } // System.out.printf("Episode %d \t Reward: %f \n", currentEpisode, sumOfRewards); diff --git a/src/main/java/core/controller/RLController.java b/src/main/java/core/controller/RLController.java index d855ab1..fc33239 100644 --- a/src/main/java/core/controller/RLController.java +++ b/src/main/java/core/controller/RLController.java @@ -67,17 +67,17 @@ public class RLController implements LearningListener { } protected void initListeners() { - learning.addListener(this); - new Thread(() -> { - while(true) { - printNextEpisode = true; - try { - Thread.sleep(30 * 1000); - } catch (InterruptedException e) { - e.printStackTrace(); + learning.addListener(this); + new Thread(() -> { + while(learning.isCurrentlyLearning()) { + printNextEpisode = true; + try { + Thread.sleep(30 * 1000); + } catch (InterruptedException e) { + e.printStackTrace(); + } } - } - }).start(); + }).start(); } private void initLearning() { @@ -95,7 +95,13 @@ public class RLController implements LearningListener { protected void learnMoreEpisodes(int nrOfEpisodes) { if(learning instanceof EpisodicLearning) { - ((EpisodicLearning) learning).learn(nrOfEpisodes); + if(learning.isCurrentlyLearning()){ + ((EpisodicLearning) learning).learnMoreEpisodes(nrOfEpisodes); + }else{ + new Thread(() -> { + ((EpisodicLearning) learning).learn(nrOfEpisodes); + }).start(); + } } else { throw new RuntimeException("Triggering onLearnMoreEpisodes on non-episodic learning!"); } diff --git a/src/main/java/example/JumpingDino.java b/src/main/java/example/JumpingDino.java index 41d5290..13efb4e 100644 --- a/src/main/java/example/JumpingDino.java +++ b/src/main/java/example/JumpingDino.java @@ -3,23 +3,49 @@ package example; import core.RNG; import core.algo.Method; import core.controller.RLController; +import core.controller.RLControllerGUI; import evironment.jumpingDino.DinoAction; import evironment.jumpingDino.DinoWorld; +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 JumpingDino { public static void main(String[] args) { - RNG.setSeed(55); + File file = new File("convergence.txt"); + 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); - RLController rl = new RLController<>( - new DinoWorld(false, false), - Method.MC_CONTROL_FIRST_VISIT, - DinoAction.values()); + RLController rl = new RLController<>( + new DinoWorld(false, false), + Method.MC_CONTROL_FIRST_VISIT, + DinoAction.values()); - rl.setDelay(0); - rl.setDiscountFactor(1f); - rl.setEpsilon(0.15f); - rl.setLearningRate(1f); - rl.setNrOfEpisodes(400); - rl.start(); + + rl.setDelay(0); + rl.setDiscountFactor(1f); + rl.setEpsilon(f); + rl.setLearningRate(1f); + rl.setNrOfEpisodes(20000); + rl.start(); + + } + try { + Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } + } + System.out.println("kek"); } } diff --git a/src/main/java/example/Results b/src/main/java/example/Results new file mode 100644 index 0000000..ba42fc7 --- /dev/null +++ b/src/main/java/example/Results @@ -0,0 +1,15 @@ +Method: +Epsilon = k / currentEpisode +set to 0 if Epsilon < b + +k = 1.5 +b = 0.1 => conv. 16 + +k = 1.5 +b = 0.02 => 75 + +k = 1.4 +b = 0.02 => fail + +k = 2.0 +b = 0.02 => conv. 100 From e67f40ad65e62f2bf84ce69af91ef5573d3fcd6b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Thu, 5 Mar 2020 11:58:57 +0100 Subject: [PATCH 02/18] split DinoWorld between simple and advanced example # Conflicts: # src/main/java/example/JumpingDino.java --- .../evironment/jumpingDino/DinoWorld.java | 43 ++++++----------- .../jumpingDino/DinoWorldAdvanced.java | 26 ++++++++++ src/main/java/example/DinoSampling.java | 44 ++++++++++++----- src/main/java/example/JumpingDino.java | 47 +++++-------------- 4 files changed, 83 insertions(+), 77 deletions(-) create mode 100644 src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java diff --git a/src/main/java/evironment/jumpingDino/DinoWorld.java b/src/main/java/evironment/jumpingDino/DinoWorld.java index 103fc08..9c18bd9 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorld.java +++ b/src/main/java/evironment/jumpingDino/DinoWorld.java @@ -12,24 +12,15 @@ import java.awt.*; @Getter public class DinoWorld implements Environment, Visualizable { - private Dino dino; - private Obstacle currentObstacle; - private boolean randomObstacleSpeed; - private boolean randomObstacleDistance; + protected Dino dino; + protected Obstacle currentObstacle; private DinoWorldComponent comp; - public DinoWorld(boolean randomObstacleSpeed, boolean randomObstacleDistance){ - this.randomObstacleSpeed = randomObstacleSpeed; - this.randomObstacleDistance = randomObstacleDistance; + public DinoWorld(){ dino = new Dino(Config.DINO_SIZE, Config.DINO_STARTING_X, Config.FRAME_HEIGHT - Config.GROUND_Y - Config.DINO_SIZE, 0, 0, Color.GREEN); spawnNewObstacle(); comp = new DinoWorldComponent(this); } - - public DinoWorld(){ - this(false, false); - } - private boolean ranIntoObstacle(){ Obstacle o = currentObstacle; Dino p = dino; @@ -43,7 +34,7 @@ public class DinoWorld implements Environment, Visualizable { return xAxis && yAxis; } - private int getDistanceToObstacle(){ + protected int getDistanceToObstacle(){ return currentObstacle.getX() - dino.getX() + Config.DINO_SIZE; } @@ -78,27 +69,20 @@ public class DinoWorld implements Environment, Visualizable { done = true; } - return new StepResultEnvironment(new DinoStateWithSpeed(getDistanceToObstacle(), dino.isInJump(), getCurrentObstacle().getDx()), reward, done, ""); + return new StepResultEnvironment(generateReturnState(), reward, done, ""); } + protected State generateReturnState(){ + return new DinoState(getDistanceToObstacle(), dino.isInJump()); + } - private void spawnNewObstacle(){ + protected void spawnNewObstacle(){ int dx; int xSpawn; - if(randomObstacleSpeed){ - dx = -(int)((Math.random() + 0.5) * Config.OBSTACLE_SPEED); - }else{ - dx = -Config.OBSTACLE_SPEED; - } - - if(randomObstacleDistance){ - // randomly spawning more right outside of the screen - xSpawn = (int)(Math.random() + 0.5 * Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE); - }else{ - // instantly respawning on the left screen border - xSpawn = Config.FRAME_WIDTH + Config.OBSTACLE_SIZE; - } + dx = -Config.OBSTACLE_SPEED; + // instantly respawning on the left screen border + xSpawn = Config.FRAME_WIDTH + Config.OBSTACLE_SIZE; currentObstacle = new Obstacle(Config.OBSTACLE_SIZE, xSpawn, Config.FRAME_HEIGHT - Config.GROUND_Y - Config.OBSTACLE_SIZE, dx, 0, Color.BLACK); } @@ -106,11 +90,12 @@ public class DinoWorld implements Environment, Visualizable { private void spawnDino(){ dino = new Dino(Config.DINO_SIZE, Config.DINO_STARTING_X, Config.FRAME_HEIGHT - Config.GROUND_Y - Config.DINO_SIZE, 0, 0, Color.GREEN); } + @Override public State reset() { spawnDino(); spawnNewObstacle(); - return new DinoState(getDistanceToObstacle(), dino.isInJump()); + return generateReturnState(); } @Override diff --git a/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java b/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java new file mode 100644 index 0000000..7513ff0 --- /dev/null +++ b/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java @@ -0,0 +1,26 @@ +package evironment.jumpingDino; + +import core.State; + +import java.awt.*; + +public class DinoWorldAdvanced extends DinoWorld{ + public DinoWorldAdvanced(){ + super(); + } + + @Override + protected State generateReturnState() { + return new DinoStateWithSpeed(getDistanceToObstacle(), dino.isInJump(), getCurrentObstacle().getDx()); + } + + @Override + protected void spawnNewObstacle() { + int dx; + int xSpawn; + dx = -(int)((Math.random() + 0.5) * Config.OBSTACLE_SPEED); + // randomly spawning more right outside of the screen + xSpawn = (int)(Math.random() + 0.5 * Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE); + currentObstacle = new Obstacle(Config.OBSTACLE_SIZE, xSpawn, Config.FRAME_HEIGHT - Config.GROUND_Y - Config.OBSTACLE_SIZE, dx, 0, Color.BLACK); + } +} diff --git a/src/main/java/example/DinoSampling.java b/src/main/java/example/DinoSampling.java index 09d5c5b..38c03ff 100644 --- a/src/main/java/example/DinoSampling.java +++ b/src/main/java/example/DinoSampling.java @@ -6,22 +6,42 @@ import core.controller.RLController; import evironment.jumpingDino.DinoAction; import evironment.jumpingDino.DinoWorld; +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 void main(String[] args) { - for (int i = 0; i < 10 ; i++) { - RNG.setSeed(55); + File file = new File("convergence.txt"); + 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); - RLController rl = new RLController<>( - new DinoWorld(false, false), - Method.MC_CONTROL_FIRST_VISIT, - DinoAction.values()); + RLController rl = new RLController<>( + new DinoWorld(), + Method.MC_CONTROL_FIRST_VISIT, + DinoAction.values()); + rl.setDelay(0); + rl.setDiscountFactor(1f); + rl.setEpsilon(f); + rl.setLearningRate(1f); + rl.setNrOfEpisodes(20000); + rl.start(); - rl.setDelay(0); - rl.setDiscountFactor(1f); - rl.setEpsilon(0.15f); - rl.setLearningRate(1f); - rl.setNrOfEpisodes(400); - rl.start(); + } + try { + Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } } } } diff --git a/src/main/java/example/JumpingDino.java b/src/main/java/example/JumpingDino.java index 13efb4e..3f71ca1 100644 --- a/src/main/java/example/JumpingDino.java +++ b/src/main/java/example/JumpingDino.java @@ -7,45 +7,20 @@ import core.controller.RLControllerGUI; import evironment.jumpingDino.DinoAction; import evironment.jumpingDino.DinoWorld; -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 JumpingDino { public static void main(String[] args) { - File file = new File("convergence.txt"); - 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); + RNG.setSeed(55); - RLController rl = new RLController<>( - new DinoWorld(false, false), - Method.MC_CONTROL_FIRST_VISIT, - DinoAction.values()); + RLController rl = new RLControllerGUI<>( + new DinoWorld(), + Method.MC_CONTROL_FIRST_VISIT, + DinoAction.values()); - - rl.setDelay(0); - rl.setDiscountFactor(1f); - rl.setEpsilon(f); - rl.setLearningRate(1f); - rl.setNrOfEpisodes(20000); - rl.start(); - - } - try { - Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); - } catch (IOException e) { - e.printStackTrace(); - } - } - System.out.println("kek"); + rl.setDelay(100); + rl.setDiscountFactor(1f); + rl.setEpsilon(0.15f); + rl.setLearningRate(1f); + rl.setNrOfEpisodes(10000); + rl.start(); } } From 4641f50b791f0dd0876b862890cb9f6458e23248 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Thu, 5 Mar 2020 13:17:54 +0100 Subject: [PATCH 03/18] add results for convergence for advanced dino jumping --- convResAdvScale.png | Bin 0 -> 8067 bytes convResultAdv.png | Bin 0 -> 8856 bytes Rplot.png => convResultSimple.png | Bin convergenceAdv.txt | 20 +++++++++++++++ epsilonValues.R | 6 +++++ src/main/java/core/algo/EpisodicLearning.java | 2 +- .../evironment/jumpingDino/DinoWorld.java | 3 +++ .../jumpingDino/DinoWorldAdvanced.java | 23 ++++++++++++++++-- src/main/java/example/DinoSampling.java | 7 +++--- src/main/java/example/JumpingDino.java | 5 ++-- 10 files changed, 58 insertions(+), 8 deletions(-) create mode 100644 convResAdvScale.png create mode 100644 convResultAdv.png rename Rplot.png => 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Learning imple if(timestampCurrentEpisode > 300000){ converged = true; // t - File file = new File("convergence.txt"); + File file = new File("convergenceAdv.txt"); try { Files.writeString(Path.of(file.getPath()), currentEpisode/2 + ",", StandardOpenOption.APPEND); } catch (IOException e) { diff --git a/src/main/java/evironment/jumpingDino/DinoWorld.java b/src/main/java/evironment/jumpingDino/DinoWorld.java index 9c18bd9..9d28277 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorld.java +++ b/src/main/java/evironment/jumpingDino/DinoWorld.java @@ -10,6 +10,9 @@ import lombok.Getter; import javax.swing.*; import java.awt.*; +/** + * 57 states + */ @Getter public class DinoWorld implements Environment, Visualizable { protected Dino dino; diff --git a/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java b/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java index 7513ff0..8009795 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java +++ b/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java @@ -1,9 +1,19 @@ package evironment.jumpingDino; +import core.RNG; import core.State; import java.awt.*; +/** + * 3580 states + * if: + * dx = -(int)((Math.random() + 0.5) * Config.OBSTACLE_SPEED); + * xSpawn = Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE; + * + * 350 states + * if 4 speed variants + */ public class DinoWorldAdvanced extends DinoWorld{ public DinoWorldAdvanced(){ super(); @@ -18,9 +28,18 @@ public class DinoWorldAdvanced extends DinoWorld{ protected void spawnNewObstacle() { int dx; int xSpawn; - dx = -(int)((Math.random() + 0.5) * Config.OBSTACLE_SPEED); + double ran = RNG.getRandom().nextDouble(); + if(ran < 0.25){ + dx = -(int)(0.7 * Config.OBSTACLE_SPEED); + }else if(ran < 0.5){ + dx = -(int)(1.3 * Config.OBSTACLE_SPEED); + }else if(ran < 0.75){ + dx = -(int)(1.6 * Config.OBSTACLE_SPEED); + } else{ + dx = -2 * Config.OBSTACLE_SPEED; + } // randomly spawning more right outside of the screen - xSpawn = (int)(Math.random() + 0.5 * Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE); + xSpawn = Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE; currentObstacle = new Obstacle(Config.OBSTACLE_SIZE, xSpawn, Config.FRAME_HEIGHT - Config.GROUND_Y - Config.OBSTACLE_SIZE, dx, 0, Color.BLACK); } } diff --git a/src/main/java/example/DinoSampling.java b/src/main/java/example/DinoSampling.java index 38c03ff..03ed20c 100644 --- a/src/main/java/example/DinoSampling.java +++ b/src/main/java/example/DinoSampling.java @@ -5,6 +5,7 @@ import core.algo.Method; import core.controller.RLController; import evironment.jumpingDino.DinoAction; import evironment.jumpingDino.DinoWorld; +import evironment.jumpingDino.DinoWorldAdvanced; import java.io.File; import java.io.IOException; @@ -14,7 +15,7 @@ import java.nio.file.StandardOpenOption; public class DinoSampling { public static void main(String[] args) { - File file = new File("convergence.txt"); + File file = new File("convergenceAdv.txt"); for(float f = 0.05f; f <=1.003 ; f+=0.05f){ try { Files.writeString(Path.of(file.getPath()), f + ",", StandardOpenOption.APPEND); @@ -26,14 +27,14 @@ public class DinoSampling { RNG.setSeed(i *13); RLController rl = new RLController<>( - new DinoWorld(), + new DinoWorldAdvanced(), Method.MC_CONTROL_FIRST_VISIT, DinoAction.values()); rl.setDelay(0); rl.setDiscountFactor(1f); rl.setEpsilon(f); rl.setLearningRate(1f); - rl.setNrOfEpisodes(20000); + rl.setNrOfEpisodes(100000); rl.start(); } diff --git a/src/main/java/example/JumpingDino.java b/src/main/java/example/JumpingDino.java index 3f71ca1..37934c0 100644 --- a/src/main/java/example/JumpingDino.java +++ b/src/main/java/example/JumpingDino.java @@ -6,13 +6,14 @@ import core.controller.RLController; import core.controller.RLControllerGUI; import evironment.jumpingDino.DinoAction; import evironment.jumpingDino.DinoWorld; +import evironment.jumpingDino.DinoWorldAdvanced; public class JumpingDino { public static void main(String[] args) { RNG.setSeed(55); RLController rl = new RLControllerGUI<>( - new DinoWorld(), + new DinoWorldAdvanced(), Method.MC_CONTROL_FIRST_VISIT, DinoAction.values()); @@ -20,7 +21,7 @@ public class JumpingDino { rl.setDiscountFactor(1f); rl.setEpsilon(0.15f); rl.setLearningRate(1f); - rl.setNrOfEpisodes(10000); + rl.setNrOfEpisodes(1000000); rl.start(); } } From ee1d62842de876e6188c81b45d0f945c6ca06121 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sun, 15 Mar 2020 16:58:53 +0100 Subject: [PATCH 04/18] split Antworld into episodic and continuous task - add new simple state for jumping dino, to see if convergence is guarenteed with with state representation - changed reward structure for ant game --- .idea/misc.xml | 2 +- JDSimpleConvergenceResults.txt | 20 +++ build.gradle | 3 +- con.txt | 20 +++ convergenceAdv.txt | 22 +-- convergenceAdv2.txt | 6 + epsilonValues.R | 24 ++- src/main/java/core/RNG.java | 9 +- .../java/evironment/antGame/AntWorld.java | 153 ++++++++++-------- .../antGame/AntWorldContinuous.java | 36 +++++ src/main/java/evironment/antGame/Cell.java | 10 ++ .../java/evironment/antGame/Constants.java | 5 +- src/main/java/evironment/antGame/Grid.java | 55 +++++-- src/main/java/evironment/antGame/Reward.java | 4 +- .../jumpingDino/DinoStateSimple.java | 65 ++++++++ .../evironment/jumpingDino/DinoWorld.java | 2 +- .../jumpingDino/DinoWorldAdvanced.java | 25 ++- src/main/java/example/ContinuousAnt.java | 25 +++ src/main/java/example/DinoSampling.java | 3 +- src/main/java/example/JumpingDino.java | 12 +- src/main/java/example/RunningAnt.java | 6 +- 21 files changed, 372 insertions(+), 135 deletions(-) create mode 100644 JDSimpleConvergenceResults.txt create mode 100644 con.txt create mode 100644 convergenceAdv2.txt create mode 100644 src/main/java/evironment/antGame/AntWorldContinuous.java create mode 100644 src/main/java/evironment/jumpingDino/DinoStateSimple.java create mode 100644 src/main/java/example/ContinuousAnt.java diff --git a/.idea/misc.xml b/.idea/misc.xml index 384e5d8..8a27f0b 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -11,7 +11,7 @@ - + \ No newline at end of file diff --git a/JDSimpleConvergenceResults.txt b/JDSimpleConvergenceResults.txt new file mode 100644 index 0000000..e32dd0a --- /dev/null +++ b/JDSimpleConvergenceResults.txt @@ -0,0 +1,20 @@ +0.05,447,1036,447,788,499,499,880,514,583,420,759,678,529,781,742,185,848,346,733,961,657,312,599,245,1076,1361,377,612,821,481,374,250,171,949,1079,788,465,537,431,312,565,1046,825,579,1178,547,1111,518,949,779,924,430,543,1262,642,814,508,422,1120,536,591,381,670,681,382,297,795,774,1552,691,909,748,520,639,704,575,752,418,503,292,1159,1020,570,839,696,895,511,635,830,522,628,518,408,392,605,888,790,629,547,999 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a/convergenceAdv.txt b/convergenceAdv.txt index e32dd0a..1d40ebf 100644 --- a/convergenceAdv.txt +++ b/convergenceAdv.txt @@ -1,20 +1,2 @@ -0.05,447,1036,447,788,499,499,880,514,583,420,759,678,529,781,742,185,848,346,733,961,657,312,599,245,1076,1361,377,612,821,481,374,250,171,949,1079,788,465,537,431,312,565,1046,825,579,1178,547,1111,518,949,779,924,430,543,1262,642,814,508,422,1120,536,591,381,670,681,382,297,795,774,1552,691,909,748,520,639,704,575,752,418,503,292,1159,1020,570,839,696,895,511,635,830,522,628,518,408,392,605,888,790,629,547,999 -0.1,337,303,221,455,64,271,377,946,381,253,669,479,267,870,250,364,347,83,124,464,356,535,820,180,369,310,284,456,166,267,240,215,347,295,226,409,344,266,404,480,195,235,655,233,429,149,304,164,409,276,268,139,495,388,401,283,343,284,720,351,170,781,59,369,789,155,206,464,380,272,645,180,216,698,166,497,650,297,595,352,138,163,412,238,336,90,453,238,306,598,487,722,640,592,493,144,435,441,387,374 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-0.95,421,588,274,114,492,232,230,464,736,233,182,92,401,208,116,357,313,200,308,331,4166,440,575,1279,104,849,42,246,657,2075,203,85,1028,963,1433,696,221,169,168,276,166,238,434,713,1769,295,638,386,186,255,167,382,830,242,1249,260,592,1796,816,359,416,202,3354,928,220,343,596,57,146,705,313,309,500,138,482,57,309,590,913,380,311,212,286,324,950,894,532,525,231,2050,537,72,616,220,273,307,836,105,324,332 -1.00,898,274,43,1486,173,433,52,573,214,154,602,339,66,719,378,366,857,438,246,775,1006,551,390,182,479,365,473,765,487,1763,759,185,335,286,369,138,220,240,307,669,239,402,180,254,224,240,327,197,2168,363,364,139,485,1176,322,257,707,667,208,794,166,2786,769,147,525,584,329,667,1233,613,456,3296,386,216,684,956,217,1735,662,146,447,99,681,38,1009,133,258,935,229,878,283,499,304,155,611,532,1257,621,394,564 +0.05,5,5,5,5,5,5,5,5,5,5,5,5,56,56 +0.10,6,6,6,6,6,6,6,6,6 diff --git a/convergenceAdv2.txt b/convergenceAdv2.txt new file mode 100644 index 0000000..40dc883 --- /dev/null +++ b/convergenceAdv2.txt @@ -0,0 +1,6 @@ +0.05,8,64,62,15,84,5,72,183, +0.1,15,15,6,18,46,28,307, +0.15,177,20,12,9,21,4,6,65,173, +0.2,6,56,13,28,4,231,9,11,22, +0.25,92,37,11,4,310, +0.3,106,7,302,12,23,4,5,177, \ No newline at end of file diff --git a/epsilonValues.R b/epsilonValues.R index fec014b..ed356c1 100644 --- a/epsilonValues.R +++ b/epsilonValues.R @@ -1,6 +1,26 @@ # Libraries library(ggplot2) +library(matrixStats) +# file.choose() convergence <- read.csv(file.choose(), header=FALSE, row.names=1) -men <- rowMeans(convergence[,-1]) -ba <- barplot(names=rownames(convergence), height=men, ylim=c(0,max(men) +100), ylab = "avg. episodes until convergence", xlab = "epsilon value") + +sds <- rowSds(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) +men <- rowMeans(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) +print(sds) + +# create dummy data +data <- data.frame( + names=rownames(convergence), + means=men, + sds=sds +) + +ggplot(data) + + geom_bar(aes(x=names, y=means), stat="identity", fill="skyblue", alpha=0.7) + + geom_errorbar( aes(x=names, ymin=means-sds, ymax=means+sds), width=0.4, colour="orange", alpha=0.9, size=1.3) + + geom_text(aes(label=as.integer(means), x =names, y=means), position=position_dodge(width=0.9), vjust=-0.25) + + xlab("Epsilon") + ylab("avg. amount of episodes until convergence") + + +ba <- barplot(names=rownames(convergence), height=men, ylim=c(0, max(men)*1.2), ylab = "avg. episodes until convergence", xlab = "epsilon value") text(x = ba, y = men, label = as.integer(men), pos = 3, cex = 0.8, col = "red") diff --git a/src/main/java/core/RNG.java b/src/main/java/core/RNG.java index 1bc01e3..b0708af 100644 --- a/src/main/java/core/RNG.java +++ b/src/main/java/core/RNG.java @@ -1,9 +1,10 @@ package core; - -import java.security.SecureRandom; import java.util.Random; /** + * ! SecureRandom not working properly on windows/different JDKs, + * using Random again ! + * * To ensure deterministic behaviour of repeating program executions, * this class is used for all random number generation methods. * Do not use Math.random()! @@ -13,10 +14,10 @@ import java.util.Random; * execution) */ public class RNG { - private static SecureRandom rng; + private static Random rng; private static int seed = 123; static { - rng = new SecureRandom(); + rng = new Random(); rng.setSeed(seed); } diff --git a/src/main/java/evironment/antGame/AntWorld.java b/src/main/java/evironment/antGame/AntWorld.java index ebd0de6..d7aaa94 100644 --- a/src/main/java/evironment/antGame/AntWorld.java +++ b/src/main/java/evironment/antGame/AntWorld.java @@ -9,16 +9,19 @@ import evironment.antGame.gui.AntWorldComponent; import javax.swing.*; import java.awt.*; +/** + * Episodic AntWorld + */ public class AntWorld implements Environment, Visualizable { /** * */ - private Grid grid; + protected Grid grid; /** * Intern (backend) representation of the ant. * The AntWorld essentially acts like the game host of the original AntGame. */ - private Ant myAnt; + protected Ant myAnt; /** * The client agent. In the original AntGame the host would send jade messages * of the current observation to each client on every tick. @@ -32,13 +35,13 @@ public class AntWorld implements Environment, Visualizable { * through an intern grid clone (brain), for example. A history as mentioned in * various lectures could be possible as well. */ - private AntAgent antAgent; + protected AntAgent antAgent; - private int tick; + protected int tick; private int maxEpisodeTicks; - public AntWorld(int width, int height, double foodDensity){ - grid = new Grid(width, height, foodDensity); + public AntWorld(int width, int height) { + grid = new Grid(width, height); antAgent = new AntAgent(width, height); myAnt = new Ant(); maxEpisodeTicks = 1000; @@ -46,73 +49,68 @@ public class AntWorld implements Environment, Visualizable { } public AntWorld(){ - this(Constants.DEFAULT_GRID_WIDTH, Constants.DEFAULT_GRID_HEIGHT, Constants.DEFAULT_FOOD_DENSITY); + this(Constants.DEFAULT_GRID_WIDTH, Constants.DEFAULT_GRID_HEIGHT); } - @Override - public StepResultEnvironment step(AntAction action){ - AntObservation observation; - State newState; - double reward = 0; - String info = ""; - boolean done = false; - + protected StepCalculation processStep(AntAction action) { + StepCalculation sc = new StepCalculation(); + sc.reward = -1; + sc.info = ""; + sc.done = false; Cell currentCell = grid.getCell(myAnt.getPos()); - Point potentialNextPos = new Point(myAnt.getPos().x, myAnt.getPos().y); - boolean stayOnCell = true; + sc.potentialNextPos = new Point(myAnt.getPos().x, myAnt.getPos().y); + sc.stayOnCell = true; // flag to enable a check if all food has been collected only fired if food was dropped // on the starting position - boolean checkCompletion = false; + sc.checkCompletion = false; - switch (action) { + switch(action) { case MOVE_UP: - potentialNextPos.y -= 1; - stayOnCell = false; + sc.potentialNextPos.y -= 1; + sc.stayOnCell = false; break; case MOVE_RIGHT: - potentialNextPos.x += 1; - stayOnCell = false; + sc.potentialNextPos.x += 1; + sc.stayOnCell = false; break; case MOVE_DOWN: - potentialNextPos.y += 1; - stayOnCell = false; + sc.potentialNextPos.y += 1; + sc.stayOnCell = false; break; case MOVE_LEFT: - potentialNextPos.x -= 1; - stayOnCell = false; + sc.potentialNextPos.x -= 1; + sc.stayOnCell = false; break; case PICK_UP: - if(myAnt.hasFood()){ + if(myAnt.hasFood()) { // Ant tries to pick up food but can only hold one piece - reward = Reward.FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY; - }else if(currentCell.getFood() == 0){ + 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 - reward = Reward.FOOD_PICK_UP_FAIL_NO_FOOD; - }else if(currentCell.getFood() > 0){ + sc.reward += Reward.FOOD_PICK_UP_FAIL_NO_FOOD; + } else if(currentCell.getFood() > 0) { // Ant successfully picks up food currentCell.setFood(currentCell.getFood() - 1); myAnt.setHasFood(true); - reward = Reward.FOOD_PICK_UP_SUCCESS; + sc.reward = Reward.FOOD_PICK_UP_SUCCESS; } break; case DROP_DOWN: - if(!myAnt.hasFood()){ + if(!myAnt.hasFood()) { // Ant had no food to drop - reward = Reward.FOOD_DROP_DOWN_FAIL_NO_FOOD; - }else{ - // Drop food onto the ground - currentCell.setFood(currentCell.getFood() + 1); + 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){ - reward = Reward.FOOD_DROP_DOWN_FAIL_NOT_START; - done = true; - }else{ - reward = Reward.FOOD_DROP_DOWN_SUCCESS; + if(currentCell.getType() != CellType.START) { + sc.reward += Reward.FOOD_DROP_DOWN_FAIL_NOT_START; + // Drop food onto the ground + currentCell.setFood(currentCell.getFood() + 1); + } else { + sc.reward = Reward.FOOD_DROP_DOWN_SUCCESS; myAnt.setPoints(myAnt.getPoints() + 1); - checkCompletion = true; + sc.checkCompletion = true; } } break; @@ -121,24 +119,33 @@ public class AntWorld implements Environment, Visualizable { } // movement action was selected - if(!stayOnCell){ - if(!isInGrid(potentialNextPos)){ - stayOnCell = true; - reward = Reward.RAN_INTO_WALL; - }else if(hitObstacle(potentialNextPos)){ - stayOnCell = true; - reward = Reward.RAN_INTO_OBSTACLE; + if(!sc.stayOnCell) { + if(!isInGrid(sc.potentialNextPos)) { + sc.stayOnCell = true; + sc.reward += Reward.RAN_INTO_WALL; + } else if(hitObstacle(sc.potentialNextPos)) { + sc.stayOnCell = true; + sc.reward += Reward.RAN_INTO_OBSTACLE; } } + return sc; + } + + @Override + public StepResultEnvironment step(AntAction action){ + AntObservation observation; + State newState; + + StepCalculation sc = processStep(action); + // valid movement - if(!stayOnCell){ - myAnt.getPos().setLocation(potentialNextPos); + if(!sc.stayOnCell) { + myAnt.getPos().setLocation(sc.potentialNextPos); if(antAgent.getCell(myAnt.getPos()).getType() == CellType.UNKNOWN){ // the ant will move to a cell that was previously unknown - reward = Reward.UNKNOWN_FIELD_EXPLORED; - }else{ - reward = 0; + // TODO: not optimal for going straight for food + // sc.reward = Reward.UNKNOWN_FIELD_EXPLORED; } } @@ -148,33 +155,36 @@ public class AntWorld implements Environment, Visualizable { // let the ant agent process the observation to create a valid markov state newState = antAgent.feedObservation(observation); - if(checkCompletion){ - done = grid.isAllFoodCollected(); + if(sc.checkCompletion) { + sc.done = grid.isAllFoodCollected(); } - - /* - if(!done){ - reward = -1; - } - */ if(++tick == maxEpisodeTicks){ - done = true; + sc.done = true; } - - StepResultEnvironment result = new StepResultEnvironment(newState, reward, done, info); - return result; + return new StepResultEnvironment(newState, sc.reward, sc.done, sc.info); } - private boolean isInGrid(Point pos){ + protected boolean isInGrid(Point pos) { return pos.x >= 0 && pos.x < grid.getWidth() && pos.y >= 0 && pos.y < grid.getHeight(); } - private boolean hitObstacle(Point pos){ + protected boolean hitObstacle(Point pos) { return grid.getCell(pos).getType() == CellType.OBSTACLE; } + protected class StepCalculation { + double reward; + String info; + boolean done; + Point potentialNextPos = new Point(myAnt.getPos().x, myAnt.getPos().y); + boolean stayOnCell = true; + // flag to enable a check if all food has been collected only fired if food was dropped + // on the starting position + boolean checkCompletion = false; + } + public State reset() { grid.resetWorld(); antAgent.initUnknownWorld(); @@ -189,6 +199,7 @@ public class AntWorld implements Environment, Visualizable { public void setMaxEpisodeLength(int maxTicks){ this.maxEpisodeTicks = maxTicks; } + public Point getSpawningPoint(){ return grid.getStartPoint(); } diff --git a/src/main/java/evironment/antGame/AntWorldContinuous.java b/src/main/java/evironment/antGame/AntWorldContinuous.java new file mode 100644 index 0000000..0800ea6 --- /dev/null +++ b/src/main/java/evironment/antGame/AntWorldContinuous.java @@ -0,0 +1,36 @@ +package evironment.antGame; + +import core.StepResultEnvironment; + +public class AntWorldContinuous extends AntWorld { + public AntWorldContinuous(int width, int height) { + super(width, height); + } + + public AntWorldContinuous() { + super(); + } + + @Override + public StepResultEnvironment step(AntAction action) { + AntObservation observation; + Cell currentCell = grid.getCell(myAnt.getPos()); + + StepCalculation sc = processStep(action); + + // flag is set to true if food gets dropped onto starts + if(sc.checkCompletion) { + grid.spawnNewFood(); + } + // valid movement + if(!sc.stayOnCell) { + myAnt.getPos().setLocation(sc.potentialNextPos); + } + + // get observation after action was computed + observation = new AntObservation(grid.getCell(myAnt.getPos()), myAnt.getPos(), myAnt.hasFood()); + + return new StepResultEnvironment(new AntState(grid.getGrid(), observation.getPos(), observation.hasFood()), sc.reward, false, sc.info); + } + +} diff --git a/src/main/java/evironment/antGame/Cell.java b/src/main/java/evironment/antGame/Cell.java index 5fccc5d..0133270 100644 --- a/src/main/java/evironment/antGame/Cell.java +++ b/src/main/java/evironment/antGame/Cell.java @@ -7,6 +7,7 @@ import java.awt.*; public class Cell { @Getter + @Setter private CellType type; @Getter @Setter @@ -38,4 +39,13 @@ public class Cell { } return super.equals(obj); } + + @Override + public String toString() { + return "Cell{" + + "type=" + type + + ", food=" + food + + ", pos=" + pos + + '}'; + } } diff --git a/src/main/java/evironment/antGame/Constants.java b/src/main/java/evironment/antGame/Constants.java index 10077ba..2394491 100644 --- a/src/main/java/evironment/antGame/Constants.java +++ b/src/main/java/evironment/antGame/Constants.java @@ -1,7 +1,6 @@ package evironment.antGame; public class Constants { - public static final int DEFAULT_GRID_WIDTH = 10; - public static final int DEFAULT_GRID_HEIGHT = 10; - public static final double DEFAULT_FOOD_DENSITY = 0.1; + public static final int DEFAULT_GRID_WIDTH = 5; + public static final int DEFAULT_GRID_HEIGHT = 5; } diff --git a/src/main/java/evironment/antGame/Grid.java b/src/main/java/evironment/antGame/Grid.java index dced49a..a6b9538 100644 --- a/src/main/java/evironment/antGame/Grid.java +++ b/src/main/java/evironment/antGame/Grid.java @@ -7,24 +7,18 @@ import java.awt.*; public class Grid { private int width; private int height; - private double foodDensity; private Point start; private Cell[][] grid; private Cell[][] initialGrid; - public Grid(int width, int height, double foodDensity){ + public Grid(int width, int height) { this.width = width; this.height = height; - this.foodDensity = foodDensity; grid = new Cell[width][height]; initialGrid = new Cell[width][height]; initRandomWorld(); } - public Grid(int width, int height){ - this(width, height, 0); - } - public void resetWorld(){ grid = Util.deepCopyCellGrid(initialGrid); } @@ -32,15 +26,52 @@ public class Grid { public void initRandomWorld(){ for(int x = 0; x < width; ++x){ for(int y = 0; y < height; ++y){ - if( RNG.getRandom().nextDouble() < foodDensity){ - initialGrid[x][y] = new Cell(new Point(x,y), CellType.FREE, 1); - }else{ - initialGrid[x][y] = new Cell(new Point(x,y), CellType.FREE); - } + initialGrid[x][y] = new Cell(new Point(x, y), CellType.FREE); } } start = new Point(RNG.getRandom().nextInt(width), RNG.getRandom().nextInt(height)); initialGrid[start.x][start.y] = new Cell(new Point(start.x, start.y), CellType.START); + spawnNewFood(initialGrid); + spawnObstacles(); + } + + //TODO + private void spawnObstacles() { + initialGrid[3][1].setType(CellType.OBSTACLE); + initialGrid[4][1].setType(CellType.OBSTACLE); + initialGrid[5][1].setType(CellType.OBSTACLE); + initialGrid[6][1].setType(CellType.OBSTACLE); + initialGrid[7][1].setType(CellType.OBSTACLE); + initialGrid[3][2].setType(CellType.OBSTACLE); + initialGrid[3][3].setType(CellType.OBSTACLE); + initialGrid[3][4].setType(CellType.OBSTACLE); + initialGrid[4][4].setType(CellType.OBSTACLE); + initialGrid[5][4].setType(CellType.OBSTACLE); + initialGrid[6][4].setType(CellType.OBSTACLE); + } + + /** + * Spawns one additional food on a random field EXCEPT for the starting position + */ + public void spawnNewFood(Cell[][] grid) { + boolean foodSpawned = false; + Point potFood = new Point(0, 0); + CellType potFieldType; + while(!foodSpawned) { + potFood.x = RNG.getRandom().nextInt(width); + potFood.y = RNG.getRandom().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); + foodSpawned = true; + System.out.println("spawned new food at " + potFood); + System.out.println(initialGrid[potFood.x][potFood.y]); + } + } + } + + public void spawnNewFood() { + spawnNewFood(grid); } public Point getStartPoint(){ diff --git a/src/main/java/evironment/antGame/Reward.java b/src/main/java/evironment/antGame/Reward.java index 62f294a..05e7a1f 100644 --- a/src/main/java/evironment/antGame/Reward.java +++ b/src/main/java/evironment/antGame/Reward.java @@ -7,9 +7,9 @@ public class Reward { public static final double FOOD_DROP_DOWN_FAIL_NO_FOOD = -1; public static final double FOOD_DROP_DOWN_FAIL_NOT_START = -1; - public static final double FOOD_DROP_DOWN_SUCCESS = 1; + public static final double FOOD_DROP_DOWN_SUCCESS = 40; - public static final double UNKNOWN_FIELD_EXPLORED = 1; + public static final double UNKNOWN_FIELD_EXPLORED = 0; public static final double RAN_INTO_WALL = -1; public static final double RAN_INTO_OBSTACLE = -1; diff --git a/src/main/java/evironment/jumpingDino/DinoStateSimple.java b/src/main/java/evironment/jumpingDino/DinoStateSimple.java new file mode 100644 index 0000000..4552489 --- /dev/null +++ b/src/main/java/evironment/jumpingDino/DinoStateSimple.java @@ -0,0 +1,65 @@ +package evironment.jumpingDino; + +import core.State; +import core.gui.Visualizable; +import lombok.AllArgsConstructor; +import lombok.Getter; + +import javax.swing.*; +import java.awt.*; +import java.io.Serializable; +import java.util.Objects; + +@AllArgsConstructor +@Getter +public class DinoStateSimple implements State, Serializable, Visualizable { + protected final double scale = 0.5; + private int xDistanceToObstacle; + + @Override + public String toString() { + return "DinoState{" + + "xDistanceToObstacle=" + xDistanceToObstacle + + '}'; + } + + @Override + public boolean equals(Object o) { + if(this == o) return true; + if(o == null || getClass() != o.getClass()) return false; + DinoStateSimple dinoState = (DinoStateSimple) o; + return xDistanceToObstacle == dinoState.xDistanceToObstacle; + } + + @Override + public int hashCode() { + return Objects.hash(xDistanceToObstacle); + } + + @Override + public JComponent visualize() { + return new JComponent() { + { + setPreferredSize(new Dimension(Config.FRAME_WIDTH, (int) (scale * Config.FRAME_HEIGHT))); + setVisible(true); + } + + @Override + protected void paintComponent(Graphics g) { + super.paintComponents(g); + drawObjects(g); + } + }; + } + + public void drawObjects(Graphics g) { + g.setColor(Color.BLACK); + g.fillRect(0, (int) (scale * (Config.FRAME_HEIGHT - Config.GROUND_Y)), Config.FRAME_WIDTH, 2); + + g.fillRect((int) (scale * Config.DINO_STARTING_X), (int) (scale * (Config.FRAME_HEIGHT - Config.GROUND_Y - Config.DINO_SIZE)), (int) (scale * Config.DINO_SIZE), (int) (scale * Config.DINO_SIZE)); + g.drawString("Distance: " + xDistanceToObstacle, (int) (scale * Config.DINO_STARTING_X), (int) (scale * (Config.FRAME_HEIGHT - Config.GROUND_Y - Config.OBSTACLE_SIZE - 40))); + + g.fillRect((int) (scale * (Config.DINO_STARTING_X + getXDistanceToObstacle())), (int) (scale * (Config.FRAME_HEIGHT - Config.GROUND_Y - Config.OBSTACLE_SIZE)), (int) (scale * Config.OBSTACLE_SIZE), (int) (scale * Config.OBSTACLE_SIZE)); + + } +} diff --git a/src/main/java/evironment/jumpingDino/DinoWorld.java b/src/main/java/evironment/jumpingDino/DinoWorld.java index 9d28277..5b60cd7 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorld.java +++ b/src/main/java/evironment/jumpingDino/DinoWorld.java @@ -76,7 +76,7 @@ public class DinoWorld implements Environment, Visualizable { } protected State generateReturnState(){ - return new DinoState(getDistanceToObstacle(), dino.isInJump()); + return new DinoStateSimple(getDistanceToObstacle()); } protected void spawnNewObstacle(){ diff --git a/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java b/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java index 8009795..a2ae420 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java +++ b/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java @@ -13,6 +13,9 @@ import java.awt.*; * * 350 states * if 4 speed variants + * + * 2044 + * 4 speeds, 4 distance */ public class DinoWorldAdvanced extends DinoWorld{ public DinoWorldAdvanced(){ @@ -21,7 +24,7 @@ public class DinoWorldAdvanced extends DinoWorld{ @Override protected State generateReturnState() { - return new DinoStateWithSpeed(getDistanceToObstacle(), dino.isInJump(), getCurrentObstacle().getDx()); + return new DinoStateWithSpeed(getDistanceToObstacle(), dino.isInJump(), currentObstacle.getDx()); } @Override @@ -30,16 +33,26 @@ public class DinoWorldAdvanced extends DinoWorld{ int xSpawn; double ran = RNG.getRandom().nextDouble(); if(ran < 0.25){ - dx = -(int)(0.7 * Config.OBSTACLE_SPEED); + dx = -(int) (0.35 * Config.OBSTACLE_SPEED); }else if(ran < 0.5){ - dx = -(int)(1.3 * Config.OBSTACLE_SPEED); + dx = -(int) (0.7 * Config.OBSTACLE_SPEED); }else if(ran < 0.75){ dx = -(int)(1.6 * Config.OBSTACLE_SPEED); } else{ - dx = -2 * Config.OBSTACLE_SPEED; + dx = -(int) (3.5 * Config.OBSTACLE_SPEED); + } + double ran2 = RNG.getRandom().nextDouble(); + if(ran2 < 0.25) { + // randomly spawning more right outside of the screen + xSpawn = Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE; + + } else if(ran2 < 0.5) { + xSpawn = (int) (1.08 * Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE); + } else if(ran2 < 0.75) { + xSpawn = (int) (1.11 * Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE); + } else { + xSpawn = (int) (1.23 * Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE); } - // randomly spawning more right outside of the screen - xSpawn = Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE; currentObstacle = new Obstacle(Config.OBSTACLE_SIZE, xSpawn, Config.FRAME_HEIGHT - Config.GROUND_Y - Config.OBSTACLE_SIZE, dx, 0, Color.BLACK); } } diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java new file mode 100644 index 0000000..a20ae7d --- /dev/null +++ b/src/main/java/example/ContinuousAnt.java @@ -0,0 +1,25 @@ +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; + +public class ContinuousAnt { + public static void main(String[] args) { + RNG.setSeed(56); + RLController rl = new RLControllerGUI<>( + new AntWorldContinuous(8, 8), + Method.Q_LEARNING_OFF_POLICY_CONTROL, + AntAction.values()); + + rl.setDelay(200); + rl.setNrOfEpisodes(10000); + rl.setDiscountFactor(0.95f); + rl.setEpsilon(0.15f); + + rl.start(); + } +} diff --git a/src/main/java/example/DinoSampling.java b/src/main/java/example/DinoSampling.java index 03ed20c..3fbb8ef 100644 --- a/src/main/java/example/DinoSampling.java +++ b/src/main/java/example/DinoSampling.java @@ -4,7 +4,6 @@ import core.RNG; import core.algo.Method; import core.controller.RLController; import evironment.jumpingDino.DinoAction; -import evironment.jumpingDino.DinoWorld; import evironment.jumpingDino.DinoWorldAdvanced; import java.io.File; @@ -34,7 +33,7 @@ public class DinoSampling { rl.setDiscountFactor(1f); rl.setEpsilon(f); rl.setLearningRate(1f); - rl.setNrOfEpisodes(100000); + rl.setNrOfEpisodes(50000); rl.start(); } diff --git a/src/main/java/example/JumpingDino.java b/src/main/java/example/JumpingDino.java index 37934c0..66d2054 100644 --- a/src/main/java/example/JumpingDino.java +++ b/src/main/java/example/JumpingDino.java @@ -3,25 +3,23 @@ package example; import core.RNG; import core.algo.Method; import core.controller.RLController; -import core.controller.RLControllerGUI; import evironment.jumpingDino.DinoAction; import evironment.jumpingDino.DinoWorld; -import evironment.jumpingDino.DinoWorldAdvanced; public class JumpingDino { public static void main(String[] args) { - RNG.setSeed(55); + RNG.setSeed(29); - RLController rl = new RLControllerGUI<>( - new DinoWorldAdvanced(), + RLController rl = new RLController<>( + new DinoWorld(), Method.MC_CONTROL_FIRST_VISIT, DinoAction.values()); - rl.setDelay(100); + rl.setDelay(0); rl.setDiscountFactor(1f); rl.setEpsilon(0.15f); rl.setLearningRate(1f); - rl.setNrOfEpisodes(1000000); + rl.setNrOfEpisodes(30000000); rl.start(); } } diff --git a/src/main/java/example/RunningAnt.java b/src/main/java/example/RunningAnt.java index f1d393c..d01461c 100644 --- a/src/main/java/example/RunningAnt.java +++ b/src/main/java/example/RunningAnt.java @@ -12,13 +12,13 @@ public class RunningAnt { RNG.setSeed(56); RLController rl = new RLControllerGUI<>( - new AntWorld(3, 3, 0.1), - Method.MC_CONTROL_FIRST_VISIT, + new AntWorld(8, 8), + Method.Q_LEARNING_OFF_POLICY_CONTROL, AntAction.values()); rl.setDelay(200); rl.setNrOfEpisodes(10000); - rl.setDiscountFactor(1f); + rl.setDiscountFactor(0.9f); rl.setEpsilon(0.15f); rl.start(); From 58f9900f3cffa30e5e0f1472f81cbd5499cf3d90 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Tue, 17 Mar 2020 18:33:54 +0100 Subject: [PATCH 05/18] Delete con.txt --- con.txt | 20 -------------------- 1 file changed, 20 deletions(-) delete mode 100644 con.txt diff --git a/con.txt b/con.txt deleted file mode 100644 index 90f4d05..0000000 --- a/con.txt +++ /dev/null @@ -1,20 +0,0 @@ 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-0.65,2927,2667,6480,3327,1148,20622,1159,1995,11561,1387,3423,8214,4808,2243,895,12123,3130,4774,18643,1420,2069,8101,1203,2343,3404,812,7383,3894,1275,7371,2794,2107,4178,4916,906,1482,6988,5286,3549,825,14992,4329,9648,12596,1520,1953,1714,16021,1544,9407,12398,1461,3339,2375,3976,6882,1225,5720,4793,2890,978,9106,1114,2978,6228,1833,1369,1516,5869,3312,1511,1897,13421,2159,5183,5017,4529,2060,9510,1367,13258,2115,2921,3235,1910,7535,3969,1416,2256,8444,4304,2881,3293,4981,3895,1728,10426,11769 -0.70,1873,4185,17340,4931,5268,2872,995,1878,6787,5225,2391,2046,796,7451,4642,2507,4279,2829,3556,7686,2601,6621,5950,1451,7073,5008,4269,19619,6826,1939,18936,2616,1352,1546,4311,2623,9623,3558,3586,7553,6106,1623,2159,5808,3135,7249,2643,4079,2036,3324,1584,8870,2322,8866,8451,6603,9716,18438,1468,7903,7222,1878,12807,5844,1268,2276,13139,8881,11004,4889,1717,5348,7186,2271,1639,2325,6008,2738,3918,3949,1502,2461,2454,5256,3672,1521,1480,7782,2869,796,12067,11323,2872 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-0.85,5493,10568,19366,5705,15430,8183,5721,13314,3753,23888,21843,6856,2803,9434,4794,10730,4484,23990,6350,16180,8099,4298,11220,4624,5946,24895,8464,4416,6619,2800,4081,12459,1981,12488,6380,9597,10328,1901,24563,13059,3639,12988,2604,4440,22666,1775,4078,5175,1144,3759,11119,1856,10831,2229,5333,17121,9698,14919,2353,3963,8189,13920,5301,16516,2446,3985 -0.90,24592,18962,2318,17604,14327,4999,9023,5562,7541,11799,8724,12642,2147,5909,5414,12572,10018,18962,3444,16813,16492,3991,7315,17775,11087,3479,2540,9597,13169,8588,2794,24864,19491,2673,5363,4799,5852,3757,1115,5798,24249,2576 -0.95,24269,14543,6828,3800,2930,12482,8749,17286,9802,7114,3756,14507,15139,1266,3794,15184,23713,6453,15157,7045,8048,6274,4713,5024,7315,8193,3695,2114,5084,12686,19158,20940,9037,15016,15530,11063,8586,15635,7196 -1.00,11364,6363,8012,19730,8425,21388,7864,18427,3126,9522,3180,4838,4078,22968,21540,4050,5612,4990,9933,1969,20399,3691,14813,14994,1577,1587,23322,22071,13903,3584,9721 From eca0d8db4d8d92e41733df69c62df86bdb84898f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Thu, 26 Mar 2020 19:22:50 +0100 Subject: [PATCH 06/18] create Dino Sampling state --- .idea/misc.xml | 2 +- JDSimpleConvergenceResults.txt | 20 -------- convergence.txt | 20 -------- convergenceAdv.txt | 2 - convergenceAdv2.txt | 6 --- convergenceAdvanced.txt | 21 +++++++++ src/main/java/core/algo/EpisodicLearning.java | 6 +-- .../algo/td/QLearningOffPolicyTDControl.java | 15 ++++-- src/main/java/core/algo/td/SARSA.java | 27 ++++++++++- src/main/java/core/policy/EpsilonPolicy.java | 4 +- .../evironment/antGame/AntStateOriginal.java | 46 +++++++++++++++++++ .../java/evironment/antGame/AntWorld.java | 17 +++---- .../antGame/AntWorldContinuous.java | 11 +++-- .../AntWorldContinuousOriginalState.java | 36 +++++++++++++++ .../java/evironment/antGame/CellType.java | 3 +- src/main/java/evironment/antGame/Grid.java | 4 +- .../evironment/jumpingDino/DinoWorld.java | 4 +- src/main/java/example/ContinuousAnt.java | 3 +- src/main/java/example/DinoSampling.java | 28 +++++++---- src/main/java/example/JumpingDino.java | 7 +-- 20 files changed, 192 insertions(+), 90 deletions(-) delete mode 100644 JDSimpleConvergenceResults.txt delete mode 100644 convergence.txt delete mode 100644 convergenceAdv.txt delete mode 100644 convergenceAdv2.txt create mode 100644 convergenceAdvanced.txt create mode 100644 src/main/java/evironment/antGame/AntStateOriginal.java create mode 100644 src/main/java/evironment/antGame/AntWorldContinuousOriginalState.java diff --git a/.idea/misc.xml b/.idea/misc.xml index 8a27f0b..384e5d8 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -11,7 +11,7 @@ - + \ No newline at end of file diff --git a/JDSimpleConvergenceResults.txt b/JDSimpleConvergenceResults.txt deleted file mode 100644 index e32dd0a..0000000 --- a/JDSimpleConvergenceResults.txt +++ /dev/null @@ -1,20 +0,0 @@ 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-0.75,815,131,219,257,95,270,177,38,283,207,451,478,422,108,331,666,573,281,139,102,179,218,87,284,274,370,173,130,235,534,420,219,174,352,234,343,337,171,302,276,1122,220,236,181,200,423,208,348,191,205,346,545,333,438,178,281,270,203,90,238,436,66,236,561,174,299,188,147,140,217,298,263,244,84,639,126,285,233,159,138,47,378,845,166,317,236,480,91,306,163,325,732,106,271,299,398,125,238,588,306 -0.80,295,191,493,205,1165,763,1031,238,69,412,56,509,356,336,97,722,106,524,239,403,493,337,270,341,112,147,448,603,356,130,336,490,854,672,253,188,375,442,308,222,625,353,216,160,265,442,263,464,143,261,686,319,160,413,237,128,147,165,391,478,632,598,333,187,463,433,253,357,134,84,671,224,273,334,185,142,666,200,112,270,113,221,1003,432,210,414,194,187,101,292,69,424,862,190,306,235,339,268,64,178 -0.85,221,61,356,234,367,238,586,474,171,168,411,157,295,304,242,118,245,361,250,856,256,194,738,391,499,213,234,411,1910,369,455,315,800,85,212,117,200,518,99,76,796,640,335,256,225,415,612,123,141,301,154,313,329,812,414,104,648,657,192,163,544,1346,305,229,131,4275,222,374,484,386,139,287,734,251,187,232,688,293,718,276,156,507,289,395,493,152,107,905,184,507,267,307,437,724,241,518,238,183,379,285 -0.90,651,606,207,546,347,1180,121,112,1135,135,203,396,245,275,354,130,320,254,255,148,291,263,448,344,237,357,211,318,700,212,234,545,240,404,277,438,391,383,402,551,392,131,740,643,393,373,288,297,403,342,288,234,319,352,869,396,277,595,87,150,833,710,129,520,619,151,150,375,860,243,282,242,296,549,426,100,890,315,505,451,184,167,643,154,56,252,147,915,463,139,227,697,386,324,292,625,66,161,420,526 -0.95,421,588,274,114,492,232,230,464,736,233,182,92,401,208,116,357,313,200,308,331,4166,440,575,1279,104,849,42,246,657,2075,203,85,1028,963,1433,696,221,169,168,276,166,238,434,713,1769,295,638,386,186,255,167,382,830,242,1249,260,592,1796,816,359,416,202,3354,928,220,343,596,57,146,705,313,309,500,138,482,57,309,590,913,380,311,212,286,324,950,894,532,525,231,2050,537,72,616,220,273,307,836,105,324,332 -1.00,898,274,43,1486,173,433,52,573,214,154,602,339,66,719,378,366,857,438,246,775,1006,551,390,182,479,365,473,765,487,1763,759,185,335,286,369,138,220,240,307,669,239,402,180,254,224,240,327,197,2168,363,364,139,485,1176,322,257,707,667,208,794,166,2786,769,147,525,584,329,667,1233,613,456,3296,386,216,684,956,217,1735,662,146,447,99,681,38,1009,133,258,935,229,878,283,499,304,155,611,532,1257,621,394,564 diff --git a/convergence.txt b/convergence.txt deleted file mode 100644 index ba6b6c3..0000000 --- a/convergence.txt +++ /dev/null @@ -1,20 +0,0 @@ -0.05,300,2426,159,339,537,264,1223,298,314,295,177,343,272,256,214,105,87,2851,395,4537,9444,1,7466,25,1371,1020,112,5354,358,48,181,2327,112,21,682,504,2593,637,179,348,215,1294,1222,177,4454,974,1,24,4844,95,961,24,900,424,72,81,572,464,1280,1104,118,345,1748,506,2558,92,1977,804,1884,640,351,3124,2849,2360,708,143,177,209,69,1479,602,2108,2864,223,928,190,5142,79,774,540,1882,966,226,2262,295,2267,1536,103,244,2202 -0.1,76,346,1058,131,17,773,58,168,78,3976,3294,548,271,22,135,543,408,441,33,988,911,337,82,1368,1107,3645,6,64,783,388,110,1096,189,347,36,1115,46,79,76,385,400,1455,333,1259,224,417,17,75,1095,334,6,711,150,28,510,54,759,1,931,156,1176,221,218,404,661,221,103,44,154,56,116,3316,348,338,130,257,1243,278,150,488,65,1063,33,77,17,827,51,674,885,374,1354,1245,1391,87,70,420,47,118,484,51 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-0.65,73,139,22,146,3,111,16,12,55,51,129,164,59,21,1,198,93,81,119,40,168,51,88,2,49,32,112,146,80,183,49,40,36,231,29,52,102,22,193,160,1,28,21,55,136,90,141,315,154,103,42,2,146,40,46,51,60,24,95,13,215,12,37,250,146,64,8,59,215,120,200,28,139,21,64,23,61,5,181,161,157,74,261,97,39,68,125,444,118,198,67,33,17,194,30,254,79,94,37,210 -0.70,74,103,14,73,123,115,128,93,12,54,1,34,78,294,77,27,5,17,46,31,255,154,83,59,66,152,125,103,28,94,73,79,110,290,3,100,208,42,28,193,120,278,170,141,169,45,143,121,145,127,91,97,126,287,80,97,79,83,102,109,241,280,247,26,90,84,104,78,99,218,17,256,380,135,18,17,121,72,119,50,35,46,54,219,55,113,185,178,140,55,121,155,126,4,8,115,40,13,98,12 -0.75,9,91,100,130,203,86,54,44,1,52,51,43,116,135,121,13,482,59,2,74,98,18,85,137,57,106,116,161,60,38,7,46,54,228,204,120,97,405,175,74,199,151,35,42,371,93,197,17,94,128,191,301,201,55,106,126,178,116,369,37,35,71,26,102,153,62,34,47,14,89,102,124,98,152,12,117,136,1,146,72,219,38,61,106,34,22,27,56,120,48,6,6,29,57,98,9,208,39,390,147 -0.80,42,45,81,110,48,158,63,40,60,106,52,90,129,105,81,193,54,67,164,79,302,86,237,160,204,213,18,50,37,85,168,148,84,213,13,25,185,25,8,20,69,78,312,62,255,50,248,144,223,3,34,161,132,138,113,39,60,83,65,20,94,96,196,51,30,58,147,22,84,102,109,189,79,124,196,58,147,69,33,130,82,10,290,77,39,48,76,158,202,60,178,20,132,39,19,85,186,108,35,222 -0.85,74,12,115,22,86,14,503,244,15,89,81,186,17,79,133,87,4,98,68,30,105,214,33,46,9,105,173,309,117,147,54,84,27,293,347,136,17,57,120,54,235,98,90,155,71,274,4,327,128,460,93,122,196,98,148,51,125,113,72,325,70,202,39,186,42,37,31,45,154,20,80,61,24,32,66,127,43,85,90,146,27,146,78,60,166,213,67,199,119,21,158,128,147,16,37,28,2,145,282,40 -0.90,31,147,81,271,124,26,142,48,49,103,61,114,62,188,135,220,56,100,107,267,4,372,165,227,26,226,180,241,50,83,39,217,480,208,95,58,55,95,28,66,370,32,37,10,11,292,85,470,173,185,1,331,116,593,47,365,140,88,189,73,17,15,250,100,483,210,65,72,89,52,48,53,47,164,94,4,141,143,140,263,50,135,1,152,67,68,59,166,114,11,41,224,26,40,81,224,132,49,439,41 -0.95,183,71,44,60,53,174,226,160,83,147,83,203,83,146,331,218,2,111,17,152,238,127,187,108,66,55,88,51,49,193,158,249,132,276,383,215,365,31,136,181,284,21,92,120,55,107,340,296,135,13,76,293,301,193,119,57,23,232,23,119,76,137,68,135,118,42,67,202,155,156,57,178,259,221,22,102,69,97,75,164,83,137,46,165,80,33,279,118,65,30,4,134,6,28,161,23,16,403,172,66 -1.00,264,486,133,12,484,71,319,98,96,110,251,22,221,201,39,6,28,384,280,105,41,226,298,135,341,117,125,432,180,89,73,146,199,17,70,354,264,50,137,147,343,393,332,59,128,44,21,569,123,285,110,153,15,168,142,361,216,331,22,338,132,124,371,28,4,77,125,466,48,231,254,231,8,287,96,114,162,201,154,247,220,133,123,2,175,71,213,280,74,25,343,480,70,206,22,23,92,107,204,91 diff --git a/convergenceAdv.txt b/convergenceAdv.txt deleted file mode 100644 index 1d40ebf..0000000 --- a/convergenceAdv.txt +++ /dev/null @@ -1,2 +0,0 @@ -0.05,5,5,5,5,5,5,5,5,5,5,5,5,56,56 -0.10,6,6,6,6,6,6,6,6,6 diff --git a/convergenceAdv2.txt b/convergenceAdv2.txt deleted file mode 100644 index 40dc883..0000000 --- a/convergenceAdv2.txt +++ /dev/null @@ -1,6 +0,0 @@ -0.05,8,64,62,15,84,5,72,183, -0.1,15,15,6,18,46,28,307, -0.15,177,20,12,9,21,4,6,65,173, -0.2,6,56,13,28,4,231,9,11,22, -0.25,92,37,11,4,310, -0.3,106,7,302,12,23,4,5,177, \ No newline at end of file diff --git a/convergenceAdvanced.txt b/convergenceAdvanced.txt new file mode 100644 index 0000000..827d701 --- /dev/null +++ b/convergenceAdvanced.txt @@ -0,0 +1,21 @@ +# 4 speeds, 4 distance. Distance, inJump and incomming speed; Monte Carlo +0.05,8565,3803,4010,30744,5419,1733,30335,4070,7502,7880,12776,2242,10489,4384,23578,14718,17801,7770,4097,5905,4027,5969,8492,1994,1542,18577,2346,13550,2279,5072,39861,15109,4419,10970,6150,2359,3093,7131,5932,18531,6887,22726,4925,3320,6832,13704,10372,25447,22945,28991,4526,12461,1831,20964,21963,2749,7112,3260,33941,14907,9168,7133,6590,21075,5302,2546,10164,3101,3204,7850,40776,38750,32898,14204,4431,12038,13430,13976,7385,7647,9634,12230,6635,9532,4181,12857,10473,2609,3827,4800,4316,5114,4153,6282,4737,5078,7358,11660,4750,16256 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+0.3,5105,2480,2842,1427,860,5434,2914,3608,2814,2037,775,654,2607,1418,786,1093,6668,967,2877,3091,2022,1799,2963,741,1352,1126,915,2413,1938,1709,2025,1192,1787,2065,1319,4872,1162,1752,3669,971,1702,3016,718,5211,2023,2113,1912,1419,1271,1323,2039,2624,2886,1404,1914,872,1292,1102,3544,2138,953,1698,800,2866,1492,1640,1270,12637,2851,3677,2295,4025,16097,2613,2591,2223,4112,1563,2441,1120,1886,2588,1559,1690,3034,937,847,6815,1616,1398,2439,1087,1603,841,4226,2110,1412,1852,2030,1651 +0.35,2154,1462,564,1702,1024,1400,960,2879,1226,2021,3619,951,4514,878,1796,2912,2112,1128,6131,733,2392,1413,2338,1437,1694,1440,1667,1714,1025,1625,2729,1200,4780,1431,2054,4221,21191,2106,2524,1742,1743,2546,1435,2707,1590,1228,2792,6297,1181,2126,2354,4585,736,1181,797,1287,1167,2023,947,628,2975,1069,1803,1558,4170,1695,3664,2169,1026,1381,1914,1497,1547,1508,3400,1533,1706,1511,973,1514,1334,833,3001,1891,1171,1797,1867,1107,1066,1180,710,2204,2308,2090,4976,3246,3289,2952,2053,1858 +0.4,1358,2175,1946,3255,3528,1126,3074,3633,2082,3648,1478,2562,3231,2679,1471,2027,1011,2322,2608,1063,1191,2552,2487,1648,4293,1907,1158,1239,1650,2524,1264,1676,2506,2911,1078,1664,2119,1234,4422,1787,1264,1923,2271,617,1489,2358,1089,2072,3087,1224,1261,761,2000,1166,1197,3519,816,2670,3148,1543,2279,973,2901,2117,2817,2979,1035,2630,501,884,901,1088,3228,1340,2681,1404,999,880,2641,2097,1168,5745,1426,1621,2584,1248,860,867,1987,844,2770,1839,1387,8928,1918,2712,1874,4033,2830,1160 +0.45,952,3145,3339,1530,2135,1357,2356,3650,2091,3197,2500,2467,1558,1743,2915,1107,1560,1467,1984,4573,1161,1532,2155,3748,3453,1670,3841,2159,6404,1278,1013,3146,3325,1537,1202,3885,3577,847,3355,1316,773,1269,7931,759,3015,1318,1848,1707,3255,2499,1592,3410,1890,1461,1025,1330,2008,2333,838,798,4177,3636,4507,3540,3155,1250,2368,4013,1714,1045,1268,2063,3622,1994,2293,1280,2493,3504,1371,1539,1616,722,2119,6346,1275,2904,4186,3192,1409,2475,966,2020,1789,2340,2919,1735,3885,1880,2238,1097 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+0.6,3791,2167,4108,1529,6057,10906,1848,3916,2554,2260,10363,3852,1868,4647,2310,2445,4288,6644,4171,1847,3360,3840,9454,3431,5358,1506,3422,2613,8244,1601,3807,2675,1066,10286,1533,12266,1587,6858,1335,4647,6590,4669,6556,1865,4763,3360,1336,2594,3571,1451,3525,4525,10153,1950,1550,1622,4162,4054,3396,2066,7273,5209,1565,17691,3451,6348,8727,5952,1031,1667,1334,1739,4680,2429,4557,2444,1913,2060,5295,15284,4703,1610,5416,1454,5184,3950,1121,1682,836,28294,1488,3644,8243,1917,9651,5226,3574,1816,11354,3791 +0.65,2927,2667,6480,3327,1148,20622,1159,1995,11561,1387,3423,8214,4808,2243,895,12123,3130,4774,18643,1420,2069,8101,1203,2343,3404,812,7383,3894,1275,7371,2794,2107,4178,4916,906,1482,6988,5286,3549,825,14992,45974,4329,9648,12596,1520,1953,1714,16021,1544,9407,12398,1461,3339,2375,3976,6370,1225,5720,4793,2890,978,9106,35243,1114,2978,6228,1833,1369,1516,5869,3312,1511,1897,13421,2159,5183,5017,4529,2060,9510,1367,13258,2115,2921,3235,1910,7535,3969,1416,2256,8444,4304,2881,3293,4981,3895,1728,10426,11769 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+0.75,5316,6466,2819,1521,8721,5240,2167,7393,23803,2967,11932,37884,9338,17412,11452,16091,1828,11745,2245,24697,1786,25564,5518,2423,2680,7215,7624,4498,6302,19095,1255,26793,8959,2094,11525,7324,13853,2935,23283,11041,3041,5165,22986,30790,3130,33125,21531,13741,6549,40701,3985,3451,14035,5976,13429,6650,5788,19359,7972,4733,13785,3008,2927,5636,4063,1151,1713,4341,22161,2441,3818,27604,2231,11920,17832,13841,16102,8596,3899,11222,35054,940,47249,10479,10952,2209,36197,19993,12426,4239,13743,4139,10398,2877,1111,4726,67426,32716,16350,5256 +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 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+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 +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 diff --git a/src/main/java/core/algo/EpisodicLearning.java b/src/main/java/core/algo/EpisodicLearning.java index 49bde9f..a14cba0 100644 --- a/src/main/java/core/algo/EpisodicLearning.java +++ b/src/main/java/core/algo/EpisodicLearning.java @@ -6,6 +6,7 @@ import core.LearningConfig; import core.StepResult; import core.listener.LearningListener; import core.policy.EpsilonGreedyPolicy; +import example.DinoSampling; import lombok.Getter; import lombok.Setter; @@ -104,10 +105,10 @@ public abstract class EpisodicLearning extends Learning imple timestamp++; timestampCurrentEpisode++; // TODO: more sophisticated way to check convergence - if(timestampCurrentEpisode > 300000){ + if(timestampCurrentEpisode > 30000000){ converged = true; // t - File file = new File("convergenceAdv.txt"); + File file = new File(DinoSampling.FILE_NAME); try { Files.writeString(Path.of(file.getPath()), currentEpisode/2 + ",", StandardOpenOption.APPEND); } catch (IOException e) { @@ -127,7 +128,6 @@ public abstract class EpisodicLearning extends Learning imple private void startLearning(){ dispatchLearningStart(); while(episodesToLearn.get() > 0){ - dispatchEpisodeStart(); timestampCurrentEpisode = 0; nextEpisode(); diff --git a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java index 274e2ab..69919fa 100644 --- a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java +++ b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java @@ -39,14 +39,23 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin sumOfRewards = 0; while(envResult == null || !envResult.isDone()) { actionValues = stateActionTable.getActionValues(state); - A action = policy.chooseAction(actionValues); - + A action; + 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){ + state = nextState; + dispatchStepEnd(); + continue; + } // Q Update double currentQValue = stateActionTable.getActionValues(state).get(action); // maxQ(S', a); diff --git a/src/main/java/core/algo/td/SARSA.java b/src/main/java/core/algo/td/SARSA.java index 10c9346..336516d 100644 --- a/src/main/java/core/algo/td/SARSA.java +++ b/src/main/java/core/algo/td/SARSA.java @@ -3,12 +3,15 @@ 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 SARSA extends EpisodicLearning { private float alpha; + private Policy greedyPolicy = new GreedyPolicy<>(); public SARSA(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, float learningRate, int delay) { super(environment, actionSpace, discountFactor, delay); @@ -32,10 +35,18 @@ public class SARSA extends EpisodicLearning { StepResultEnvironment envResult = null; Map actionValues = stateActionTable.getActionValues(state); - A action = policy.chooseAction(actionValues); + A action; + 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(); @@ -44,8 +55,20 @@ public class SARSA extends EpisodicLearning { // Pick next action actionValues = stateActionTable.getActionValues(nextState); - A nextAction = policy.chooseAction(actionValues); + A nextAction; + 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); diff --git a/src/main/java/core/policy/EpsilonPolicy.java b/src/main/java/core/policy/EpsilonPolicy.java index 7c34006..e6da16e 100644 --- a/src/main/java/core/policy/EpsilonPolicy.java +++ b/src/main/java/core/policy/EpsilonPolicy.java @@ -1,8 +1,8 @@ package core.policy; /** - * Chooses the action with the highest values with possibility: 1-Ɛ + Ɛ/|A| - * With possibility of Ɛ, a random action is taken (highest values option included). + * Chooses the action with the highest values with possibility: 1-Epsilon + Epsilon/|A| + * With possibility of Epsilon, a random action is taken (highest values option included). * * @param Enum class of available action in specific environment */ diff --git a/src/main/java/evironment/antGame/AntStateOriginal.java b/src/main/java/evironment/antGame/AntStateOriginal.java new file mode 100644 index 0000000..6911e87 --- /dev/null +++ b/src/main/java/evironment/antGame/AntStateOriginal.java @@ -0,0 +1,46 @@ +package evironment.antGame; + +import core.State; +import lombok.AllArgsConstructor; + +import java.util.Objects; + +@AllArgsConstructor +public class AntStateOriginal implements State { + private final int currentFood; + private final int row; + private final int col; + private final CellType type; + private final int smell; + private final int food; + + @Override + public boolean equals(Object o) { + if (this == o) return true; + if (o == null || getClass() != o.getClass()) return false; + AntStateOriginal that = (AntStateOriginal) o; + return currentFood == that.currentFood && + row == that.row && + col == that.col && + smell == that.smell && + type == that.type && + food == that.food; + } + + @Override + public int hashCode() { + return Objects.hash(currentFood, row, col, type, smell, food); + } + + @Override + public String toString() { + return "AntStateOriginal{" + + "currentFood=" + currentFood + + ", row=" + row + + ", col=" + col + + ", type=" + type + + ", smell=" + smell + + ", food=" + food + + '}'; + } +} diff --git a/src/main/java/evironment/antGame/AntWorld.java b/src/main/java/evironment/antGame/AntWorld.java index d7aaa94..9f35bfc 100644 --- a/src/main/java/evironment/antGame/AntWorld.java +++ b/src/main/java/evironment/antGame/AntWorld.java @@ -134,9 +134,6 @@ public class AntWorld implements Environment, Visualizable { @Override public StepResultEnvironment step(AntAction action){ - AntObservation observation; - State newState; - StepCalculation sc = processStep(action); // valid movement @@ -149,11 +146,7 @@ public class AntWorld implements Environment, Visualizable { } } - // get observation after action was computed - observation = new AntObservation(grid.getCell(myAnt.getPos()), myAnt.getPos(), myAnt.hasFood()); - // let the ant agent process the observation to create a valid markov state - newState = antAgent.feedObservation(observation); if(sc.checkCompletion) { sc.done = grid.isAllFoodCollected(); @@ -163,7 +156,15 @@ public class AntWorld implements Environment, Visualizable { sc.done = true; } - return new StepResultEnvironment(newState, sc.reward, sc.done, sc.info); + return new StepResultEnvironment(generateReturnState(), sc.reward, sc.done, sc.info); + } + + protected State generateReturnState(){ + // get observation after action was computed + AntObservation observation = new AntObservation(grid.getCell(myAnt.getPos()), myAnt.getPos(), myAnt.hasFood()); + + // let the ant agent process the observation to create a valid markov state + return antAgent.feedObservation(observation); } protected boolean isInGrid(Point pos) { diff --git a/src/main/java/evironment/antGame/AntWorldContinuous.java b/src/main/java/evironment/antGame/AntWorldContinuous.java index 0800ea6..cf30d2e 100644 --- a/src/main/java/evironment/antGame/AntWorldContinuous.java +++ b/src/main/java/evironment/antGame/AntWorldContinuous.java @@ -1,5 +1,6 @@ package evironment.antGame; +import core.State; import core.StepResultEnvironment; public class AntWorldContinuous extends AntWorld { @@ -13,7 +14,6 @@ public class AntWorldContinuous extends AntWorld { @Override public StepResultEnvironment step(AntAction action) { - AntObservation observation; Cell currentCell = grid.getCell(myAnt.getPos()); StepCalculation sc = processStep(action); @@ -27,10 +27,13 @@ public class AntWorldContinuous extends AntWorld { myAnt.getPos().setLocation(sc.potentialNextPos); } - // get observation after action was computed - observation = new AntObservation(grid.getCell(myAnt.getPos()), myAnt.getPos(), myAnt.hasFood()); + return new StepResultEnvironment(generateReturnState(), sc.reward, false, sc.info); + } - return new StepResultEnvironment(new AntState(grid.getGrid(), observation.getPos(), observation.hasFood()), sc.reward, false, sc.info); + @Override + protected State generateReturnState(){ + AntObservation observation = new AntObservation(grid.getCell(myAnt.getPos()), myAnt.getPos(), myAnt.hasFood()); + return new AntState(grid.getGrid(), observation.getPos(), observation.hasFood()); } } diff --git a/src/main/java/evironment/antGame/AntWorldContinuousOriginalState.java b/src/main/java/evironment/antGame/AntWorldContinuousOriginalState.java new file mode 100644 index 0000000..6a4c500 --- /dev/null +++ b/src/main/java/evironment/antGame/AntWorldContinuousOriginalState.java @@ -0,0 +1,36 @@ +package evironment.antGame; + +import core.State; + +public class AntWorldContinuousOriginalState extends AntWorldContinuous { + public AntWorldContinuousOriginalState(int width, int height) { + super(width, height); + } + + public AntWorldContinuousOriginalState() { + super(); + } + + @Override + protected State generateReturnState(){ + return new AntStateOriginal(myAnt.hasFood()? 1:0, myAnt.getPos().x, myAnt.getPos().y, grid.getCell(myAnt.getPos()).getType(), calculateSmell(), grid.getCell(myAnt.getPos()).getFood()); + } + + /** + * @return total smell of neighbour food cells + */ + private int calculateSmell(){ + int smell = 0; + int maxX = grid.getGrid().length -1; + int maxY = grid.getGrid()[0].length -1; + int antX = myAnt.getPos().x; + int antY = myAnt.getPos().y; + + smell += antY > 0 ? grid.getCell(antX, antY - 1).getFood() : 0; + smell += antY < maxY ? grid.getCell(antX, antY + 1).getFood() : 0; + smell += antX > 0 ? grid.getCell(antX - 1, antY).getFood() : 0; + smell += antX < maxX ? grid.getCell(antX + 1, antY).getFood() : 0; + + return smell; + } +} diff --git a/src/main/java/evironment/antGame/CellType.java b/src/main/java/evironment/antGame/CellType.java index 68dc66b..4e51d16 100644 --- a/src/main/java/evironment/antGame/CellType.java +++ b/src/main/java/evironment/antGame/CellType.java @@ -1,6 +1,7 @@ package evironment.antGame; -public enum CellType { + +public enum CellType{ START, FREE, OBSTACLE, diff --git a/src/main/java/evironment/antGame/Grid.java b/src/main/java/evironment/antGame/Grid.java index a6b9538..dd23e78 100644 --- a/src/main/java/evironment/antGame/Grid.java +++ b/src/main/java/evironment/antGame/Grid.java @@ -64,8 +64,8 @@ public class Grid { if(potFieldType != CellType.START && grid[potFood.x][potFood.y].getFood() == 0 && potFieldType != CellType.OBSTACLE) { grid[potFood.x][potFood.y].setFood(1); foodSpawned = true; - System.out.println("spawned new food at " + potFood); - System.out.println(initialGrid[potFood.x][potFood.y]); + // System.out.println("spawned new food at " + potFood); + // System.out.println(initialGrid[potFood.x][potFood.y]); } } } diff --git a/src/main/java/evironment/jumpingDino/DinoWorld.java b/src/main/java/evironment/jumpingDino/DinoWorld.java index 5b60cd7..06bb60f 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorld.java +++ b/src/main/java/evironment/jumpingDino/DinoWorld.java @@ -44,7 +44,7 @@ public class DinoWorld implements Environment, Visualizable { @Override public StepResultEnvironment step(DinoAction action) { boolean done = false; - int reward = 1; + int reward = 0; if(action == DinoAction.JUMP){ dino.jump(); @@ -68,7 +68,7 @@ public class DinoWorld implements Environment, Visualizable { spawnNewObstacle(); } if(ranIntoObstacle()) { - reward = 0; + reward = -1; done = true; } diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index a20ae7d..0a13e70 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -6,12 +6,13 @@ import core.controller.RLController; import core.controller.RLControllerGUI; import evironment.antGame.AntAction; import evironment.antGame.AntWorldContinuous; +import evironment.antGame.AntWorldContinuousOriginalState; public class ContinuousAnt { public static void main(String[] args) { RNG.setSeed(56); RLController rl = new RLControllerGUI<>( - new AntWorldContinuous(8, 8), + new AntWorldContinuousOriginalState(8, 8), Method.Q_LEARNING_OFF_POLICY_CONTROL, AntAction.values()); diff --git a/src/main/java/example/DinoSampling.java b/src/main/java/example/DinoSampling.java index 3fbb8ef..12fcdbc 100644 --- a/src/main/java/example/DinoSampling.java +++ b/src/main/java/example/DinoSampling.java @@ -3,7 +3,9 @@ package example; import core.RNG; import core.algo.Method; import core.controller.RLController; +import core.controller.RLControllerGUI; import evironment.jumpingDino.DinoAction; +import evironment.jumpingDino.DinoWorld; import evironment.jumpingDino.DinoWorldAdvanced; import java.io.File; @@ -13,29 +15,35 @@ import java.nio.file.Path; import java.nio.file.StandardOpenOption; public class DinoSampling { + public static final float f =0.05f; + public static final String FILE_NAME = "converge.txt"; public static void main(String[] args) { - File file = new File("convergenceAdv.txt"); - for(float f = 0.05f; f <=1.003 ; f+=0.05f){ + 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); + for (int i = 1; i <= 100; i++) { + System.out.println("seed: " + i * 13); + RNG.setSeed(i * 13); RLController rl = new RLController<>( new DinoWorldAdvanced(), - Method.MC_CONTROL_FIRST_VISIT, + Method.Q_LEARNING_OFF_POLICY_CONTROL, DinoAction.values()); rl.setDelay(0); - rl.setDiscountFactor(1f); + rl.setDiscountFactor(0.99f); rl.setEpsilon(f); - rl.setLearningRate(1f); - rl.setNrOfEpisodes(50000); + rl.setLearningRate(0.9f); + rl.setNrOfEpisodes(400000); rl.start(); - } try { Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); diff --git a/src/main/java/example/JumpingDino.java b/src/main/java/example/JumpingDino.java index 66d2054..b738fbb 100644 --- a/src/main/java/example/JumpingDino.java +++ b/src/main/java/example/JumpingDino.java @@ -5,21 +5,22 @@ 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); RLController rl = new RLController<>( - new DinoWorld(), + new DinoWorldAdvanced(), Method.MC_CONTROL_FIRST_VISIT, DinoAction.values()); rl.setDelay(0); rl.setDiscountFactor(1f); - rl.setEpsilon(0.15f); + rl.setEpsilon(0.05f); rl.setLearningRate(1f); - rl.setNrOfEpisodes(30000000); + rl.setNrOfEpisodes(100000); rl.start(); } } From 328fc8521407aa6ba9036ad383ec1bcc87dbcf53 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sat, 28 Mar 2020 12:35:33 +0100 Subject: [PATCH 07/18] modify q Learning to sample results and update R script --- epsilonValues.R | 23 ++++++++- src/main/java/core/RNG.java | 8 ++++ src/main/java/core/algo/EpisodicLearning.java | 7 +-- .../MonteCarloControlFirstVisitEGreedy.java | 19 +------- .../algo/td/QLearningOffPolicyTDControl.java | 48 ++++++++++++++----- src/main/java/core/algo/td/SARSA.java | 25 ++-------- src/main/java/evironment/antGame/Grid.java | 6 +-- .../jumpingDino/DinoWorldAdvanced.java | 4 +- src/main/java/example/ContinuousAnt.java | 25 ++++++---- 9 files changed, 97 insertions(+), 68 deletions(-) diff --git a/epsilonValues.R b/epsilonValues.R index ed356c1..ff0bd53 100644 --- a/epsilonValues.R +++ b/epsilonValues.R @@ -1,7 +1,28 @@ # Libraries library(ggplot2) library(matrixStats) -# file.choose() +ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE)) +ta <- t(ta) +dim(ta) +head(ta) + +# Create dummy data +data <- data.frame( + y=ta[,1], + y2=ta[,2], + y3=ta[,3], + y4=ta[,4], + x=seq(1, length(ta)) +) +ggplot(data, aes(x)) + + geom_line(aes(y = y, colour = "var0")) + + geom_line(aes(y = y2, colour = "var1")) + + geom_line(aes(y = y3, colour = "var2")) + + geom_line(aes(y = y4, colour = "var3")) + + scale_x_log10( breaks=c(1,5,10,15,20,50,100,200), limits=c(1,200) ) + +plot(ta, x=x*1000, log="x", type="o") + convergence <- read.csv(file.choose(), header=FALSE, row.names=1) sds <- rowSds(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) diff --git a/src/main/java/core/RNG.java b/src/main/java/core/RNG.java index b0708af..959ae59 100644 --- a/src/main/java/core/RNG.java +++ b/src/main/java/core/RNG.java @@ -15,18 +15,26 @@ import java.util.Random; */ public class RNG { private static Random rng; + private static Random rngEnv; private static int seed = 123; static { rng = new Random(); rng.setSeed(seed); + rngEnv = new Random(); + rngEnv.setSeed(seed); } public static Random getRandom() { return rng; } + public static Random getRandomEnv() { + return rngEnv; + } public static void setSeed(int seed){ RNG.seed = seed; rng.setSeed(seed); + rngEnv = new Random(); + rngEnv.setSeed(seed); } } diff --git a/src/main/java/core/algo/EpisodicLearning.java b/src/main/java/core/algo/EpisodicLearning.java index a14cba0..d7b6655 100644 --- a/src/main/java/core/algo/EpisodicLearning.java +++ b/src/main/java/core/algo/EpisodicLearning.java @@ -105,8 +105,7 @@ public abstract class EpisodicLearning extends Learning imple timestamp++; timestampCurrentEpisode++; // TODO: more sophisticated way to check convergence - if(timestampCurrentEpisode > 30000000){ - converged = true; + if(false){ // t File file = new File(DinoSampling.FILE_NAME); try { @@ -114,9 +113,7 @@ public abstract class EpisodicLearning extends Learning imple } catch (IOException e) { e.printStackTrace(); } - System.out.println("converged after: " + currentEpisode/2 + " episode!"); - episodesToLearn.set(0); - dispatchLearningEnd(); + // System.out.println("converged after: " + currentEpisode/2 + " episode!"); } } diff --git a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java b/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java index 5bc93d3..ff3247b 100644 --- a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java +++ b/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java @@ -40,16 +40,9 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic private Map, Double> returnSum; private Map, Integer> returnCount; - // t - private float epsilon; - // t - private Policy greedyPolicy = new GreedyPolicy<>(); - public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay) { super(environment, actionSpace, discountFactor, delay); - // t - this.epsilon = epsilon; this.policy = new EpsilonGreedyPolicy<>(epsilon); this.stateActionTable = new DeterministicStateActionTable<>(this.actionSpace); returnSum = new HashMap<>(); @@ -74,12 +67,7 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic while(envResult == null || !envResult.isDone()) { Map actionValues = stateActionTable.getActionValues(state); - A chosenAction; - if(currentEpisode % 2 == 1){ - chosenAction = greedyPolicy.chooseAction(actionValues); - }else{ - chosenAction = policy.chooseAction(actionValues); - } + A chosenAction = policy.chooseAction(actionValues); envResult = environment.step(chosenAction); State nextState = envResult.getState(); @@ -96,12 +84,9 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic } timestamp++; dispatchStepEnd(); - if(converged) return; } - if(currentEpisode % 2 == 1){ - return; - } + // System.out.printf("Episode %d \t Reward: %f \n", currentEpisode, sumOfRewards); Set> stateActionPairs = new LinkedHashSet<>(); diff --git a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java index 69919fa..4adfef3 100644 --- a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java +++ b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java @@ -5,7 +5,15 @@ import core.algo.EpisodicLearning; import core.policy.EpsilonGreedyPolicy; import core.policy.GreedyPolicy; import core.policy.Policy; +import evironment.antGame.Reward; +import example.ContinuousAnt; +import example.DinoSampling; +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 extends EpisodicLearning { @@ -37,25 +45,43 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin sumOfRewards = 0; + int timestampTilFood = 0; + int rewardsPer1000 = 0; + int foodCollected = 0; while(envResult == null || !envResult.isDone()) { actionValues = stateActionTable.getActionValues(state); - A action; - if(currentEpisode % 2 == 0){ - action = greedyPolicy.chooseAction(actionValues); - }else{ - action = policy.chooseAction(actionValues); - } - if(converged) return; + A action = policy.chooseAction(actionValues); + // Take a step envResult = environment.step(action); double reward = envResult.getReward(); State nextState = envResult.getState(); sumOfRewards += reward; - if(currentEpisode % 2 == 0){ - state = nextState; - dispatchStepEnd(); - continue; + + rewardsPer1000+=reward; + timestampTilFood++; + + if(foodCollected == 10000){ + File file = new File(ContinuousAnt.FILE_NAME); + try { + Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } + return; } + if(reward == Reward.FOOD_DROP_DOWN_SUCCESS){ + foodCollected++; + File file = new File(ContinuousAnt.FILE_NAME); + try { + Files.writeString(Path.of(file.getPath()), timestampTilFood + ",", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } + timestampTilFood = 0; + rewardsPer1000 = 0; + } + // Q Update double currentQValue = stateActionTable.getActionValues(state).get(action); // maxQ(S', a); diff --git a/src/main/java/core/algo/td/SARSA.java b/src/main/java/core/algo/td/SARSA.java index 336516d..b64d59e 100644 --- a/src/main/java/core/algo/td/SARSA.java +++ b/src/main/java/core/algo/td/SARSA.java @@ -11,7 +11,6 @@ import java.util.Map; public class SARSA extends EpisodicLearning { private float alpha; - private Policy greedyPolicy = new GreedyPolicy<>(); public SARSA(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, float learningRate, int delay) { super(environment, actionSpace, discountFactor, delay); @@ -35,18 +34,13 @@ public class SARSA extends EpisodicLearning { StepResultEnvironment envResult = null; Map actionValues = stateActionTable.getActionValues(state); - A action; - if(currentEpisode % 2 == 1){ - action = greedyPolicy.chooseAction(actionValues); - }else{ - action = policy.chooseAction(actionValues); - } + A 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 extends EpisodicLearning { // Pick next action actionValues = stateActionTable.getActionValues(nextState); - A nextAction; - 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; - } + A nextAction = policy.chooseAction(actionValues); + // td update // target = reward + gamma * Q(nextState, nextAction) double currentQValue = stateActionTable.getActionValues(state).get(action); diff --git a/src/main/java/evironment/antGame/Grid.java b/src/main/java/evironment/antGame/Grid.java index dd23e78..0393b93 100644 --- a/src/main/java/evironment/antGame/Grid.java +++ b/src/main/java/evironment/antGame/Grid.java @@ -29,7 +29,7 @@ public class Grid { initialGrid[x][y] = new Cell(new Point(x, y), CellType.FREE); } } - start = new Point(RNG.getRandom().nextInt(width), RNG.getRandom().nextInt(height)); + start = new Point(RNG.getRandomEnv().nextInt(width), RNG.getRandomEnv().nextInt(height)); initialGrid[start.x][start.y] = new Cell(new Point(start.x, start.y), CellType.START); spawnNewFood(initialGrid); spawnObstacles(); @@ -58,8 +58,8 @@ public class Grid { Point potFood = new Point(0, 0); CellType potFieldType; while(!foodSpawned) { - potFood.x = RNG.getRandom().nextInt(width); - potFood.y = RNG.getRandom().nextInt(height); + potFood.x = RNG.getRandomEnv().nextInt(width); + 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); diff --git a/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java b/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java index a2ae420..7c35579 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java +++ b/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.getRandom().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.getRandom().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; diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index 0a13e70..9005956 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -8,19 +8,28 @@ import evironment.antGame.AntAction; import evironment.antGame.AntWorldContinuous; import evironment.antGame.AntWorldContinuousOriginalState; +import java.io.File; +import java.io.IOException; + public class ContinuousAnt { + public static final String FILE_NAME = "converge05.txt"; public static void main(String[] args) { + File file = new File(FILE_NAME); + try { + file.createNewFile(); + } catch (IOException e) { + e.printStackTrace(); + } RNG.setSeed(56); - RLController rl = new RLControllerGUI<>( - new AntWorldContinuousOriginalState(8, 8), + RLController rl = new RLController<>( + new AntWorldContinuous(8, 8), Method.Q_LEARNING_OFF_POLICY_CONTROL, AntAction.values()); - - rl.setDelay(200); - rl.setNrOfEpisodes(10000); - rl.setDiscountFactor(0.95f); - rl.setEpsilon(0.15f); - + rl.setDelay(0); + rl.setNrOfEpisodes(1); + rl.setDiscountFactor(0.7f); + rl.setLearningRate(0.2f); + rl.setEpsilon(0.5f); rl.start(); } } From 78955a95211d641bb4516300233ffec0fbe868ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sun, 29 Mar 2020 17:22:01 +0200 Subject: [PATCH 08/18] add antGame analysis data and R Scripts and images --- AdvancedZ3B1MonteCarlo.png | Bin 0 -> 18582 bytes AntAnalysis05Disc.png | Bin 0 -> 28556 bytes AntAnalysis099Disc.png | Bin 0 -> 34712 bytes AntAnalysis09Disc.png | Bin 0 -> 27974 bytes NeededTimestampsFromEverywhere.png | Bin 0 -> 13379 bytes SimpleZ1B2QLearning.png | Bin 0 -> 18871 bytes SimpleZ1B2SARSA.png | Bin 0 -> 18563 bytes SimpleZ2B1MonteCarlo.png | Bin 0 -> 18298 bytes SimpleZ2B1QLearning.png | Bin 0 -> 18791 bytes SimpleZ2B1QLearningMaxValueAsDefault.png | Bin 0 -> 20347 bytes SimpleZ2B2QLearning.png | Bin 0 -> 18467 bytes SimpleZ2B2SARSA.png | Bin 0 -> 18163 bytes SimpleZ2B4QLearning.png | Bin 0 -> 17258 bytes convResAdvScale.png | Bin 8067 -> 0 bytes convResultAdv.png | Bin 8856 -> 0 bytes convResultSimple.png | Bin 7659 -> 0 bytes convergenceAdvanced.txt | 2 +- epsilonValues.R | 51 +++++++++++++++--- optimalityDiffDiscount.png | Bin 0 -> 18230 bytes .../algo/td/QLearningOffPolicyTDControl.java | 48 +++++++++++++---- src/main/java/evironment/antGame/Reward.java | 4 +- src/main/java/example/ContinuousAnt.java | 13 +++-- src/main/java/example/JumpingDino.java | 4 +- 23 files changed, 97 insertions(+), 25 deletions(-) create mode 100644 AdvancedZ3B1MonteCarlo.png create mode 100644 AntAnalysis05Disc.png create mode 100644 AntAnalysis099Disc.png create mode 100644 AntAnalysis09Disc.png create mode 100644 NeededTimestampsFromEverywhere.png create mode 100644 SimpleZ1B2QLearning.png create mode 100644 SimpleZ1B2SARSA.png create mode 100644 SimpleZ2B1MonteCarlo.png create mode 100644 SimpleZ2B1QLearning.png create mode 100644 SimpleZ2B1QLearningMaxValueAsDefault.png create mode 100644 SimpleZ2B2QLearning.png create 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z8AHiPqW7w|+$ZiMPfnFvFW3OY_4hxTdcuj|$4g*Od2kOX)3-VoKjil1txVo7&mC^{ zG}PKRBvv*HyIm2BdGq?|&*kY)GwYN6Evl*(Z>ur2J~LkJC?vFaGT1ntkMyE4pu+Fh z?BoZrW>HF6LQ{B3sfw>j6<$>}4m&xw2Uk2ZhLpK@r(P|V{Hwq8AnGh55{UKMcSfJz zAbi>+(g^%o6dxxpiPOmr1BwB?kDDHBC?2V*YI~DzKI5>XJ<1HnITK;5gnXe6*u1_L zdBAsagwj@cVSv=5-7W3!tvWGmJiw>c;^OsCS+&1lqQKuh%TK4gf0jPy{u1I@hx^9w zjDXY?8_ci+J&rs=IyqR>jWMH8T;+|~+A~9A)bbUam?>rczi$4iHGZ@3eYk6RHg5xb zuVUl(yAKARvJN{9w7l@HxKrJWws7HF@0; extends EpisodicLearnin int timestampTilFood = 0; int rewardsPer1000 = 0; int foodCollected = 0; + int iterations = 0; + int foodTimestampsTotal= 0; while(envResult == null || !envResult.isDone()) { actionValues = stateActionTable.getActionValues(state); A action = policy.chooseAction(actionValues); @@ -57,11 +59,10 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin double reward = envResult.getReward(); State nextState = envResult.getState(); sumOfRewards += reward; - rewardsPer1000+=reward; timestampTilFood++; - if(foodCollected == 10000){ + /* if(iterations == 100){ File file = new File(ContinuousAnt.FILE_NAME); try { Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); @@ -69,17 +70,46 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin e.printStackTrace(); } return; - } + }*/ + + if(reward == Reward.FOOD_DROP_DOWN_SUCCESS){ foodCollected++; - File file = new File(ContinuousAnt.FILE_NAME); - try { - Files.writeString(Path.of(file.getPath()), timestampTilFood + ",", StandardOpenOption.APPEND); - } catch (IOException e) { - e.printStackTrace(); + foodTimestampsTotal += timestampTilFood; + if(foodCollected % 1000 == 0){ + System.out.println(foodTimestampsTotal/1000f); + File file = new File(ContinuousAnt.FILE_NAME); + try { + Files.writeString(Path.of(file.getPath()), foodTimestampsTotal/1000f +",", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } + foodTimestampsTotal = 0; } + if(foodCollected == 1000){ + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.15f); + } + if(foodCollected == 2000){ + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.10f); + } + if(foodCollected == 3000){ + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.05f); + } + if(foodCollected == 4000){ + System.out.println("final 0 expl"); + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.00f); + } + if(foodCollected == 15000){ + File file = new File(ContinuousAnt.FILE_NAME); + try { + Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } + return; + } + iterations++; timestampTilFood = 0; - rewardsPer1000 = 0; } // Q Update diff --git a/src/main/java/evironment/antGame/Reward.java b/src/main/java/evironment/antGame/Reward.java index 05e7a1f..3a910b8 100644 --- a/src/main/java/evironment/antGame/Reward.java +++ b/src/main/java/evironment/antGame/Reward.java @@ -1,13 +1,13 @@ package evironment.antGame; public class Reward { - public static final double FOOD_PICK_UP_SUCCESS = 1; + public static final double FOOD_PICK_UP_SUCCESS = 0; public static final double FOOD_PICK_UP_FAIL_NO_FOOD = -1; public static final double FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY = -1; public static final double FOOD_DROP_DOWN_FAIL_NO_FOOD = -1; public static final double FOOD_DROP_DOWN_FAIL_NOT_START = -1; - 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; diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index 9005956..08308fa 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -12,8 +12,10 @@ import java.io.File; import java.io.IOException; public class ContinuousAnt { - public static final String FILE_NAME = "converge05.txt"; + public static final String FILE_NAME = "converge22.txt"; public static void main(String[] args) { + int i = 4+4+4+6+6+6+8+10+12+14+14+16+16+16+18+18+18+20+20+20+22+22+22+24+24+24+24+26+26+26+26+26+28+28+28+28+28+30+30+30+30+32+32+32+34+34+34+36+36+38+40+42; + System.out.println(i/52f); File file = new File(FILE_NAME); try { file.createNewFile(); @@ -27,9 +29,12 @@ public class ContinuousAnt { AntAction.values()); rl.setDelay(0); rl.setNrOfEpisodes(1); - rl.setDiscountFactor(0.7f); - rl.setLearningRate(0.2f); - rl.setEpsilon(0.5f); + //0.99 0.9 0.5 + //0.99 0.95 0.9 0.7 0.5 0.3 0.1 + rl.setDiscountFactor(0.05f); + // 0.1, 0.3, 0.5, 0.7 0.9 + rl.setLearningRate(0.9f); + rl.setEpsilon(0.2f); rl.start(); } } diff --git a/src/main/java/example/JumpingDino.java b/src/main/java/example/JumpingDino.java index b738fbb..0a03b16 100644 --- a/src/main/java/example/JumpingDino.java +++ b/src/main/java/example/JumpingDino.java @@ -17,9 +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(); } From e7404a8d24342e211d1ce1393a334629f28b6c9b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Tue, 31 Mar 2020 17:49:15 +0200 Subject: [PATCH 10/18] add improved result graphs --- AdvancedZ3B1MonteCarloA.png | Bin 0 -> 26312 bytes AdvancedZ3B3MonteA.png | Bin 0 -> 25195 bytes DiscAndLRonQLearningAdvanced.R | 18 +++++++++--------- EpsilonAnalysis.R | 4 +++- OptimalityDifferentDiscountFactors.R | 6 +++--- SimpleZ1B2QLearningA.png | Bin 0 -> 26973 bytes SimpleZ1B2SarsaA.png | Bin 0 -> 26617 bytes SimpleZ2B1MonteCarloA.png | Bin 0 -> 23452 bytes SimpleZ2B1QLearningA.png | Bin 0 -> 25646 bytes SimpleZ2B1QLearningMaxA.png | Bin 0 -> 26121 bytes SimpleZ2B2QLearningA.png | Bin 0 -> 25035 bytes SimpleZ2B2SarsaA.png | Bin 0 -> 24656 bytes SimpleZ2B4QLearningA.png | Bin 0 -> 23893 bytes antGameAnalysis05DiscA.png | Bin 0 -> 33826 bytes antGameAnalysis099DiscA.png | Bin 0 -> 38423 bytes antGameAnalysis09DiscA.png | Bin 0 -> 33116 bytes convergenceAdvanced.txt | 2 +- convergenceAdvancedMCnegRew.txt | 2 ++ optimalityDiffDiscA.png | Bin 0 -> 26293 bytes 19 files changed, 18 insertions(+), 14 deletions(-) create mode 100644 AdvancedZ3B1MonteCarloA.png create mode 100644 AdvancedZ3B3MonteA.png create mode 100644 SimpleZ1B2QLearningA.png create mode 100644 SimpleZ1B2SarsaA.png create mode 100644 SimpleZ2B1MonteCarloA.png create mode 100644 SimpleZ2B1QLearningA.png create mode 100644 SimpleZ2B1QLearningMaxA.png create mode 100644 SimpleZ2B2QLearningA.png create mode 100644 SimpleZ2B2SarsaA.png create mode 100644 SimpleZ2B4QLearningA.png create mode 100644 antGameAnalysis05DiscA.png create mode 100644 antGameAnalysis099DiscA.png create mode 100644 antGameAnalysis09DiscA.png create mode 100644 optimalityDiffDiscA.png diff --git a/AdvancedZ3B1MonteCarloA.png b/AdvancedZ3B1MonteCarloA.png new file mode 100644 index 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- * 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; - *

- * 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... - *

- * How to encounter this problem? - * + * 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 */ public class MonteCarloControlFirstVisitEGreedy extends EpisodicLearning { private Map, Double> returnSum; private Map, Integer> returnCount; + private boolean isEveryVisit; - public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay) { + public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace 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 environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay) { + this(environment, actionSpace, discountFactor, epsilon, delay, false); + } + public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace actionSpace, int delay) { this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, delay); } @@ -89,35 +77,47 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic // System.out.printf("Episode %d \t Reward: %f \n", currentEpisode, sumOfRewards); - Set> stateActionPairs = new LinkedHashSet<>(); + HashMap, List> stateActionPairs = new LinkedHashMap<>(); + int firstOccurrenceIndex = 0; for(StepResult sr : episode) { - stateActionPairs.add(new ImmutablePair<>(sr.getState(), sr.getAction())); + Pair pair = new ImmutablePair<>(sr.getState(), sr.getAction()); + if(!stateActionPairs.containsKey(pair)) { + List 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 stateActionPair : stateActionPairs) { - int firstOccurenceIndex = 0; - // find first occurance of state action pair - for(StepResult sr : episode) { - if(stateActionPair.getKey().equals(sr.getState()) && stateActionPair.getValue().equals(sr.getAction())) { - break; + for(Map.Entry, List> entry : stateActionPairs.entrySet()) { + Pair stateActionPair = entry.getKey(); + List firstOccurrences = entry.getValue(); + for(Integer firstOccurrencesIdx : firstOccurrences) { + double G = 0; + 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); diff --git a/src/main/java/core/controller/RLController.java b/src/main/java/core/controller/RLController.java index fc33239..895102a 100644 --- a/src/main/java/core/controller/RLController.java +++ b/src/main/java/core/controller/RLController.java @@ -51,7 +51,11 @@ public class RLController 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: From a40e279f486bb5b3ee9fc53533f359f53a2b588a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sat, 4 Apr 2020 14:41:58 +0200 Subject: [PATCH 13/18] change reward function for antgame to match BA --- src/main/java/evironment/antGame/AntWorld.java | 12 ++++++------ src/main/java/evironment/antGame/Reward.java | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/src/main/java/evironment/antGame/AntWorld.java b/src/main/java/evironment/antGame/AntWorld.java index 0a71a31..c9f9453 100644 --- a/src/main/java/evironment/antGame/AntWorld.java +++ b/src/main/java/evironment/antGame/AntWorld.java @@ -84,10 +84,10 @@ public class AntWorld implements Environment, 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, 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, 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; } } diff --git a/src/main/java/evironment/antGame/Reward.java b/src/main/java/evironment/antGame/Reward.java index c2e008d..2ab8f9a 100644 --- a/src/main/java/evironment/antGame/Reward.java +++ b/src/main/java/evironment/antGame/Reward.java @@ -3,15 +3,15 @@ package evironment.antGame; public class Reward { public static final double DEFAULT_REWARD = -1; public static final double FOOD_PICK_UP_SUCCESS = 0; - public static final double FOOD_PICK_UP_FAIL_NO_FOOD = -1; - 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_NOT_START = -1; + public static final double FOOD_DROP_DOWN_FAIL_NO_FOOD = -2; + public static final double FOOD_DROP_DOWN_FAIL_NOT_START = -2; 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_OBSTACLE = -1; + public static final double RAN_INTO_WALL = -2; + public static final double RAN_INTO_OBSTACLE = -2; } From 595451e88b77814fb16da06ae4af3504c821bf29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sat, 4 Apr 2020 17:06:15 +0200 Subject: [PATCH 14/18] add new results of needed timestamps in total --- OptimalityAdancedDiffDiscounts.txt | 9 ---- OptimalityDifferentDiscountFactorTotalTS.R | 33 ++++++++++++ OptimalityDifferentDiscountFactors.R | 43 +++++++-------- optDiscNew.txt | 9 ++++ optDiscTimestampsNew.txt | 9 ++++ optDiscTotalTS.png | Bin 0 -> 122086 bytes optimalityDiffDiscA.png | Bin 26293 -> 0 bytes optimalityDiffDiscount.png | Bin 18230 -> 0 bytes src/main/java/core/RNG.java | 6 +-- .../algo/td/QLearningOffPolicyTDControl.java | 16 +++--- src/main/java/example/ContinuousAnt.java | 50 ++++++++++++------ 11 files changed, 115 insertions(+), 60 deletions(-) delete mode 100644 OptimalityAdancedDiffDiscounts.txt create mode 100644 OptimalityDifferentDiscountFactorTotalTS.R create mode 100644 optDiscNew.txt create mode 100644 optDiscTimestampsNew.txt create mode 100644 optDiscTotalTS.png delete mode 100644 optimalityDiffDiscA.png delete mode 100644 optimalityDiffDiscount.png diff --git a/OptimalityAdancedDiffDiscounts.txt b/OptimalityAdancedDiffDiscounts.txt deleted file mode 100644 index 6f3b51d..0000000 --- a/OptimalityAdancedDiffDiscounts.txt +++ /dev/null @@ -1,9 +0,0 @@ -// Discount factor per row 0.99 0.95 0.9 0.7 0.5 0.3 0.1 0.05; col: 1000 foodCollected -2227.291,293.974,167.684,117.521,96.234,76.003,42.731,85.039,34.988,51.885,50.998,28.669,41.673,52.083,30.215 -537.895,59.768,41.445,27.915,25.43,24.127,22.922,23.301,23.769,22.993,22.633,22.866,22.95,23.823,22.323 -286.021,37.013,29.013,24.655,23.493,23.244,22.643,23.159,23.582,22.924,22.508,22.844,22.951,23.686,22.229 -120.419,32.193,26.495,23.832,23.36,23.116,22.624,23.132,23.568,22.886,22.48,22.842,22.932,23.674,22.222 -93.303,31.302,26.482,23.775,23.36,23.116,22.624,23.132,23.568,22.886,22.48,22.842,22.932,23.674,22.222 -82.588,31.458,26.56,23.885,23.36,23.116,22.624,23.132,23.568,22.886,22.48,22.842,22.932,23.674,22.222 -87.161,34.038,28.755,25.818,25.476,24.604,24.102,25.556,25.074,25.03,24.418,24.88,24.432,25.524,23.53 -107.639,54.045,50.417,44.948,44.374,42.504,41.216,43.762,44.542,42.036,43.182,43.158,41.802,48.35,39.682 diff --git a/OptimalityDifferentDiscountFactorTotalTS.R b/OptimalityDifferentDiscountFactorTotalTS.R new file mode 100644 index 0000000..7af1295 --- /dev/null +++ b/OptimalityDifferentDiscountFactorTotalTS.R @@ -0,0 +1,33 @@ +# Libraries +library(ggplot2) +library(matrixStats) +ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1)) +ta <- t(ta) +dim(ta) +head(ta) +# Create dummy data +data <- data.frame( + y=ta[,1], + y2=ta[,2], + y3=ta[,3], + y4=ta[,4], + y5=ta[,5], + y6=ta[,6], + y7=ta[,7], + y8=ta[,8], + x=seq(1, length(ta[,1])) +) +ggplot(data, aes(x*1000)) + + labs( x ="Gesammeltes Futter", y = "Zeitstempel insgesamt", color = "Diskontierungsfaktor") + + #geom_hline(yintercept=23, linetype="dashed")+ + geom_text(aes(20000,40000,label = "opt. Verhalten", vjust = -1)) + + geom_line(aes(y = x*1000*23), size=1)+ + geom_line(aes(y = y, colour = "0.05"), size=1)+ + geom_line(aes(y = y2, colour = "0.1"), size=1) + + geom_line(aes(y = y3, colour = "0.3"), size=1) + + geom_line(aes(y = y4, colour = "0.5"), size=1) + + geom_line(aes(y = y5, colour = "0.7"), size=1)+ + geom_line(aes(y = y6, colour = "0.9"), size=1) + + geom_line(aes(y = y7, colour = "0.95"), size=1) + + geom_line(aes(y = y8, colour = "0.99"), size=1) + + theme_bw(base_size = 24) diff --git a/OptimalityDifferentDiscountFactors.R b/OptimalityDifferentDiscountFactors.R index 7f55e68..9ced426 100644 --- a/OptimalityDifferentDiscountFactors.R +++ b/OptimalityDifferentDiscountFactors.R @@ -1,37 +1,34 @@ # Libraries library(ggplot2) library(matrixStats) -ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1)) -ta <- t(ta) +#ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1)) +#ta <- t(ta) dim(ta) head(ta) - +print(ta[1:20,1]) # Create dummy data data <- data.frame( - y=ta[,1], - y2=ta[,2], - y3=ta[,3], - y4=ta[,4], - y5=ta[,5], - y6=ta[,6], - y7=ta[,7], - y8=ta[,8], - x=seq(1, length(ta[,1])) + y=ta[1:20,1], + y2=ta[1:20,2], + y3=ta[1:20,3], + y4=ta[1:20,4], + y5=ta[1:20,5], + y6=ta[1:20,6], + y7=ta[1:20,7], + y8=ta[1:20,8], + x=seq(1, length(ta[1:20,1])) ) ggplot(data, aes(x*1000)) + labs( x ="Gesamtanzahl gesammeltes Futter", y = "Ø Zeitstempel pro Futter (log)", color = "Diskontierungsfaktor") + scale_y_log10()+ geom_hline(yintercept=23, linetype="dashed")+ geom_text(aes(0,23,label = "opt", vjust = -1)) + - geom_line(aes(y = y, colour = "0.99"), size=1)+ - geom_line(aes(y = y2, colour = "0.95"), size=1) + - geom_line(aes(y = y3, colour = "0.9"), size=1) + - geom_line(aes(y = y4, colour = "0.7"), size=1) + - geom_line(aes(y = y5, colour = "0.5"), size=1)+ - geom_line(aes(y = y6, colour = "0.3"), size=1) + - geom_line(aes(y = y7, colour = "0.1"), size=1) + - geom_line(aes(y = y8, colour = "0.05"), size=1) + + geom_line(aes(y = y, colour = "0.05"), size=1)+ + geom_line(aes(y = y2, colour = "0.1"), size=1) + + geom_line(aes(y = y3, colour = "0.3"), size=1) + + geom_line(aes(y = y4, colour = "0.5"), size=1) + + geom_line(aes(y = y5, colour = "0.7"), size=1)+ + geom_line(aes(y = y6, colour = "0.9"), size=1) + + geom_line(aes(y = y7, colour = "0.95"), size=1) + + geom_line(aes(y = y8, colour = "0.99"), size=1) + theme_bw(base_size = 24) - - - \ No newline at end of file diff --git a/optDiscNew.txt b/optDiscNew.txt new file mode 100644 index 0000000..40f9006 --- /dev/null +++ b/optDiscNew.txt @@ -0,0 +1,9 @@ +# 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99 +103.857,48.501,49.283,44.149,44.51,42.402,45.102,41.896,43.104,40.76,41.0,39.134,46.756,44.282,45.834,44.942,41.078,43.66,42.812,41.672,44.022,41.196,42.106,41.384,42.174,42.736,45.792,43.482,48.132,44.14 +86.807,32.319,29.317,26.51,25.664,24.348,24.994,23.972,24.46,24.252,24.128,23.758,25.184,24.872,25.286,24.356,24.322,24.262,24.26,24.446,24.888,24.302,24.866,24.236,24.548,24.866,25.894,24.872,26.212,24.632 +81.071,30.951,26.07,24.804,23.196,22.67,23.374,22.388,22.666,22.776,22.432,22.294,23.318,23.018,23.44,22.97,22.502,22.776,22.692,22.382,23.178,22.932,23.098,22.726,22.584,23.062,23.548,22.824,23.672,22.73 +92.437,29.962,26.044,24.465,23.196,22.67,23.374,22.388,22.666,22.776,22.432,22.294,23.318,23.018,23.44,22.97,22.502,22.776,22.692,22.382,23.178,22.932,23.098,22.726,22.584,23.062,23.548,22.824,23.672,22.73 +120.382,29.993,26.154,24.518,23.196,22.67,23.374,22.388,22.666,22.776,22.432,22.294,23.318,23.018,23.44,22.97,22.502,22.776,22.692,22.382,23.178,22.932,23.098,22.726,22.584,23.062,23.548,22.824,23.672,22.73 +281.912,37.184,28.825,26.807,23.42,23.018,23.428,22.431,22.718,22.802,22.444,22.352,23.414,23.031,23.448,22.97,22.508,22.782,22.698,22.391,23.184,22.936,23.102,22.736,22.584,23.062,23.55,22.835,23.674,22.738 +536.915,54.205,42.919,33.225,26.309,23.071,23.713,22.6,22.675,22.81,22.478,22.313,23.675,23.25,23.601,23.064,22.599,22.866,23.312,22.591,23.195,23.55,23.354,22.735,22.604,23.066,23.549,22.854,23.698,22.76 +2205.811,288.101,186.619,128.568,108.486,53.722,67.856,58.127,53.659,39.438,44.677,28.751,54.808,33.351,26.386,25.356,24.812,25.128,25.056,24.712,25.626,25.316,25.552,25.112,24.874,25.446,26.05,25.17,26.16,25.058 diff --git a/optDiscTimestampsNew.txt b/optDiscTimestampsNew.txt new file mode 100644 index 0000000..7b3ec46 --- /dev/null +++ b/optDiscTimestampsNew.txt @@ -0,0 +1,9 @@ +# timestamp needed for x*1000 collected food; 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99 +103856,152357,201640,245789,290299,332701,377803,419699,462803,503563,544563,583697,630453,674735,720569,765511,806589,850249,893061,934733,978755,1019951,1062057,1103441,1145615,1188351,1234143,1277625,1325757,1369897 +86806,119125,148442,174952,200616,224964,249958,273930,298390,322642,346770,370528,395712,420584,445870,470226,494548,518810,543070,567516,592404,616706,641572,665808,690356,715222,741116,765988,792200,816832 +81070,112021,138091,162895,186091,208761,232135,254523,277189,299965,322397,344691,368009,391027,414467,437437,459939,482715,505407,527789,550967,573899,596997,619723,642307,665369,688917,711741,735413,758143 +92436,122398,148442,172907,196103,218773,242147,264535,287201,309977,332409,354703,378021,401039,424479,447449,469951,492727,515419,537801,560979,583911,607009,629735,652319,675381,698929,721753,745425,768155 +120381,150374,176528,201046,224242,246912,270286,292674,315340,338116,360548,382842,406160,429178,452618,475588,498090,520866,543558,565940,589118,612050,635148,657874,680458,703520,727068,749892,773564,796294 +281911,319095,347920,374727,398147,421165,444593,467024,489742,512544,534988,557340,580754,603785,627233,650203,672711,695493,718191,740582,763766,786702,809804,832540,855124,878186,901736,924571,948245,970983 +536914,591119,634038,667263,693572,716643,740356,762956,785631,808441,830919,853232,876907,900157,923758,946822,969421,992287,1015599,1038190,1061385,1084935,1108289,1131024,1153628,1176694,1200243,1223097,1246795,1269555 +2205810,2493911,2680530,2809098,2917584,2971306,3039162,3097289,3150948,3190386,3235063,3263814,3318622,3351973,3378359,3403715,3428527,3453655,3478711,3503423,3529049,3554365,3579917,3605029,3629903,3655349,3681399,3706569,3732729,3757787 diff --git a/optDiscTotalTS.png b/optDiscTotalTS.png new file mode 100644 index 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zG}PKRBvv*HyIm2BdGq?|&*kY)GwYN6Evl*(Z>ur2J~LkJC?vFaGT1ntkMyE4pu+Fh z?BoZrW>HF6LQ{B3sfw>j6<$>}4m&xw2Uk2ZhLpK@r(P|V{Hwq8AnGh55{UKMcSfJz zAbi>+(g^%o6dxxpiPOmr1BwB?kDDHBC?2V*YI~DzKI5>XJ<1HnITK;5gnXe6*u1_L zdBAsagwj@cVSv=5-7W3!tvWGmJiw>c;^OsCS+&1lqQKuh%TK4gf0jPy{u1I@hx^9w zjDXY?8_ci+J&rs=IyqR>jWMH8T;+|~+A~9A)bbUam?>rczi$4iHGZ@3eYk6RHg5xb zuVUl(yAKARvJN{9w7l@HxKrJWws7HF@0; extends EpisodicLearnin @Override protected void nextEpisode() { + State state = environment.reset(); try { Thread.sleep(delay); @@ -72,19 +73,19 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin }*/ - if(reward == Reward.FOOD_DROP_DOWN_SUCCESS){ + if(reward == Reward.FOOD_DROP_DOWN_SUCCESS) { foodCollected++; foodTimestampsTotal += timestampTilFood; - if(foodCollected % 1000 == 0){ - System.out.println(foodTimestampsTotal / 1000f + " " + timestampCurrentEpisode); - File file = new File(ContinuousAnt.FILE_NAME); + //System.out.println(foodCollected + " " + timestampCurrentEpisode); + File file = new File(ContinuousAnt.FILE_NAME); + if(foodCollected % 1000 == 0) { try { - Files.writeString(Path.of(file.getPath()), foodTimestampsTotal/1000f +",", StandardOpenOption.APPEND); + Files.writeString(Path.of(file.getPath()), timestampCurrentEpisode + ",", StandardOpenOption.APPEND); } catch (IOException e) { e.printStackTrace(); } - foodTimestampsTotal = 0; } + foodTimestampsTotal = 0; if(foodCollected == 1000){ ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.15f); } @@ -98,8 +99,7 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin System.out.println("final 0 expl"); ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.00f); } - if(foodCollected == 15000){ - File file = new File(ContinuousAnt.FILE_NAME); + if(foodCollected == 30000) { try { Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); } catch (IOException e) { diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index e6d89dd..0a75ec2 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -8,31 +8,47 @@ import evironment.antGame.AntWorldContinuous; import java.io.File; import java.io.IOException; +import java.util.ArrayList; +import java.util.List; public class ContinuousAnt { - public static final String FILE_NAME = "converge22.txt"; + public static final String FILE_NAME = "optDiscTimestampsNew.txt"; public static void main(String[] args) { - int i = 4+4+4+6+6+6+8+10+12+14+14+16+16+16+18+18+18+20+20+20+22+22+22+24+24+24+24+26+26+26+26+26+28+28+28+28+28+30+30+30+30+32+32+32+34+34+34+36+36+38+40+42; - System.out.println(i/52f); + int k = 4 + 4 + 4 + 6 + 6 + 6 + 8 + 10 + 12 + 14 + 14 + 16 + 16 + 16 + 18 + 18 + 18 + 20 + 20 + 20 + 22 + 22 + 22 + 24 + 24 + 24 + 24 + 26 + 26 + 26 + 26 + 26 + 28 + 28 + 28 + 28 + 28 + 30 + 30 + 30 + 30 + 32 + 32 + 32 + 34 + 34 + 34 + 36 + 36 + 38 + 40 + 42; + System.out.println(k / 52f); File file = new File(FILE_NAME); try { file.createNewFile(); } catch (IOException e) { e.printStackTrace(); } - RNG.setSeed(56); - RLController rl = new RLController<>( - new AntWorldContinuous(8, 8), - Method.Q_LEARNING_OFF_POLICY_CONTROL, - AntAction.values()); - rl.setDelay(0); - rl.setNrOfEpisodes(1); - //0.99 0.9 0.5 - //0.99 0.95 0.9 0.7 0.5 0.3 0.1 - rl.setDiscountFactor(0.1f); - // 0.1, 0.3, 0.5, 0.7 0.9 - rl.setLearningRate(0.9f); - rl.setEpsilon(0.2f); - rl.start(); + List discValues = new ArrayList<>() { + }; + discValues.add(0.05f); + discValues.add(0.1f); + discValues.add(0.3f); + discValues.add(0.5f); + discValues.add(0.7f); + discValues.add(0.9f); + discValues.add(0.95f); + discValues.add(0.99f); + + for(float disc : discValues) { + RNG.setSeed(13); + RLController rl = new RLController<>( + new AntWorldContinuous(8, 8), + Method.Q_LEARNING_OFF_POLICY_CONTROL, + AntAction.values()); + rl.setDelay(0); + rl.setNrOfEpisodes(1); + //0.99 0.9 0.5 + //0.99 0.95 0.9 0.7 0.5 0.3 0.1 + rl.setDiscountFactor(disc); + // 0.1, 0.3, 0.5, 0.7 0.9 + rl.setLearningRate(0.9f); + rl.setEpsilon(0.2f); + rl.start(); + } + } } From a08b8160a311ca51f3b11ac7095f474eef47f7f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sat, 4 Apr 2020 17:06:15 +0200 Subject: [PATCH 15/18] add new results of needed timestamps in total --- OptimalityAdancedDiffDiscounts.txt | 9 ---- OptimalityDifferentDiscountFactorTotalTS.R | 33 ++++++++++++ OptimalityDifferentDiscountFactors.R | 39 +++++++------- optDisc.png | Bin 0 -> 26655 bytes optDiscNew.txt | 9 ++++ optDiscTimestampsNew.txt | 9 ++++ optDiscTotalTS.png | Bin 0 -> 122086 bytes optimalityDiffDiscA.png | Bin 26293 -> 0 bytes optimalityDiffDiscount.png | Bin 18230 -> 0 bytes src/main/java/core/RNG.java | 6 +-- .../algo/td/QLearningOffPolicyTDControl.java | 16 +++--- src/main/java/example/ContinuousAnt.java | 50 ++++++++++++------ 12 files changed, 113 insertions(+), 58 deletions(-) delete mode 100644 OptimalityAdancedDiffDiscounts.txt create mode 100644 OptimalityDifferentDiscountFactorTotalTS.R create mode 100644 optDisc.png create mode 100644 optDiscNew.txt create mode 100644 optDiscTimestampsNew.txt create mode 100644 optDiscTotalTS.png delete mode 100644 optimalityDiffDiscA.png delete mode 100644 optimalityDiffDiscount.png diff --git a/OptimalityAdancedDiffDiscounts.txt b/OptimalityAdancedDiffDiscounts.txt deleted file mode 100644 index 6f3b51d..0000000 --- a/OptimalityAdancedDiffDiscounts.txt +++ /dev/null @@ -1,9 +0,0 @@ -// Discount factor per row 0.99 0.95 0.9 0.7 0.5 0.3 0.1 0.05; col: 1000 foodCollected -2227.291,293.974,167.684,117.521,96.234,76.003,42.731,85.039,34.988,51.885,50.998,28.669,41.673,52.083,30.215 -537.895,59.768,41.445,27.915,25.43,24.127,22.922,23.301,23.769,22.993,22.633,22.866,22.95,23.823,22.323 -286.021,37.013,29.013,24.655,23.493,23.244,22.643,23.159,23.582,22.924,22.508,22.844,22.951,23.686,22.229 -120.419,32.193,26.495,23.832,23.36,23.116,22.624,23.132,23.568,22.886,22.48,22.842,22.932,23.674,22.222 -93.303,31.302,26.482,23.775,23.36,23.116,22.624,23.132,23.568,22.886,22.48,22.842,22.932,23.674,22.222 -82.588,31.458,26.56,23.885,23.36,23.116,22.624,23.132,23.568,22.886,22.48,22.842,22.932,23.674,22.222 -87.161,34.038,28.755,25.818,25.476,24.604,24.102,25.556,25.074,25.03,24.418,24.88,24.432,25.524,23.53 -107.639,54.045,50.417,44.948,44.374,42.504,41.216,43.762,44.542,42.036,43.182,43.158,41.802,48.35,39.682 diff --git a/OptimalityDifferentDiscountFactorTotalTS.R b/OptimalityDifferentDiscountFactorTotalTS.R new file mode 100644 index 0000000..7af1295 --- /dev/null +++ b/OptimalityDifferentDiscountFactorTotalTS.R @@ -0,0 +1,33 @@ +# Libraries +library(ggplot2) +library(matrixStats) +ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1)) +ta <- t(ta) +dim(ta) +head(ta) +# Create dummy data +data <- data.frame( + y=ta[,1], + y2=ta[,2], + y3=ta[,3], + y4=ta[,4], + y5=ta[,5], + y6=ta[,6], + y7=ta[,7], + y8=ta[,8], + x=seq(1, length(ta[,1])) +) +ggplot(data, aes(x*1000)) + + labs( x ="Gesammeltes Futter", y = "Zeitstempel insgesamt", color = "Diskontierungsfaktor") + + #geom_hline(yintercept=23, linetype="dashed")+ + geom_text(aes(20000,40000,label = "opt. Verhalten", vjust = -1)) + + geom_line(aes(y = x*1000*23), size=1)+ + geom_line(aes(y = y, colour = "0.05"), size=1)+ + geom_line(aes(y = y2, colour = "0.1"), size=1) + + geom_line(aes(y = y3, colour = "0.3"), size=1) + + geom_line(aes(y = y4, colour = "0.5"), size=1) + + geom_line(aes(y = y5, colour = "0.7"), size=1)+ + geom_line(aes(y = y6, colour = "0.9"), size=1) + + geom_line(aes(y = y7, colour = "0.95"), size=1) + + geom_line(aes(y = y8, colour = "0.99"), size=1) + + theme_bw(base_size = 24) diff --git a/OptimalityDifferentDiscountFactors.R b/OptimalityDifferentDiscountFactors.R index 7f55e68..6a1f58a 100644 --- a/OptimalityDifferentDiscountFactors.R +++ b/OptimalityDifferentDiscountFactors.R @@ -5,33 +5,30 @@ ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1)) ta <- t(ta) dim(ta) head(ta) - +print(ta[1:20,1]) # Create dummy data data <- data.frame( - y=ta[,1], - y2=ta[,2], - y3=ta[,3], - y4=ta[,4], - y5=ta[,5], - y6=ta[,6], - y7=ta[,7], - y8=ta[,8], - x=seq(1, length(ta[,1])) + y=ta[1:20,1], + y2=ta[1:20,2], + y3=ta[1:20,3], + y4=ta[1:20,4], + y5=ta[1:20,5], + y6=ta[1:20,6], + y7=ta[1:20,7], + y8=ta[1:20,8], + x=seq(1, length(ta[1:20,1])) ) ggplot(data, aes(x*1000)) + labs( x ="Gesamtanzahl gesammeltes Futter", y = "Ø Zeitstempel pro Futter (log)", color = "Diskontierungsfaktor") + scale_y_log10()+ geom_hline(yintercept=23, linetype="dashed")+ geom_text(aes(0,23,label = "opt", vjust = -1)) + - geom_line(aes(y = y, colour = "0.99"), size=1)+ - geom_line(aes(y = y2, colour = "0.95"), size=1) + - geom_line(aes(y = y3, colour = "0.9"), size=1) + - geom_line(aes(y = y4, colour = "0.7"), size=1) + - geom_line(aes(y = y5, colour = "0.5"), size=1)+ - geom_line(aes(y = y6, colour = "0.3"), size=1) + - geom_line(aes(y = y7, colour = "0.1"), size=1) + - geom_line(aes(y = y8, colour = "0.05"), size=1) + + geom_line(aes(y = y, colour = "0.05"), size=1)+ + geom_line(aes(y = y2, colour = "0.1"), size=1) + + geom_line(aes(y = y3, colour = "0.3"), size=1) + + geom_line(aes(y = y4, colour = "0.5"), size=1) + + geom_line(aes(y = y5, colour = "0.7"), size=1)+ + geom_line(aes(y = y6, colour = "0.9"), size=1) + + geom_line(aes(y = y7, colour = "0.95"), size=1) + + geom_line(aes(y = y8, colour = "0.99"), size=1) + theme_bw(base_size = 24) - - - \ No newline at end of file diff --git a/optDisc.png b/optDisc.png new file mode 100644 index 0000000000000000000000000000000000000000..8def1d3f3139141ec16617d0a7b2896a37f1176c GIT binary patch literal 26655 zcmeFZc{G%N{3xz5!x-Dxx8xCpQi>!?jHQh1l%+zJ?AiCVu`4mQL`ftPB1HC2D5FA_ z$Q~&>*|(VAGYUiJp7T53bMHU*+&R-6&AgY_`?YV+J6uCeks85{AR!^4RzfRik&uv~ zNJvNlI4Ssz${|Bb5|RVC8Y-7Bfd2)*Cn1p_Au%T*r6eJpAR)z*NJx-Kn3G6gNzBbj z%!^5~@FZ0hB-M4`I8st@)C4Ido^)b@6pyFG<3&V7z{!LegqOL5Irzp(m={Z6u@YGD zvqzQivQt%!RJF5HwF5u1RU>icg#EzLB<6qS5axj|tU0#W9NS}FTx?zpe&WGtgh_-~ zq+KM=E_>MLB}pW>mL2%Ei;T>U%*I7#4@cs_Hy$fNf;9)<5?F8{a3#X47+Va!d$2v= z8=q~bnjLAEjT_D$#uuBD6q`#FW5Kt1F*uGe5PbE3W5JK&9`Fx*F}|l5kFWQ^<04gY 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zG}PKRBvv*HyIm2BdGq?|&*kY)GwYN6Evl*(Z>ur2J~LkJC?vFaGT1ntkMyE4pu+Fh z?BoZrW>HF6LQ{B3sfw>j6<$>}4m&xw2Uk2ZhLpK@r(P|V{Hwq8AnGh55{UKMcSfJz zAbi>+(g^%o6dxxpiPOmr1BwB?kDDHBC?2V*YI~DzKI5>XJ<1HnITK;5gnXe6*u1_L zdBAsagwj@cVSv=5-7W3!tvWGmJiw>c;^OsCS+&1lqQKuh%TK4gf0jPy{u1I@hx^9w zjDXY?8_ci+J&rs=IyqR>jWMH8T;+|~+A~9A)bbUam?>rczi$4iHGZ@3eYk6RHg5xb zuVUl(yAKARvJN{9w7l@HxKrJWws7HF@0; extends EpisodicLearnin @Override protected void nextEpisode() { + State state = environment.reset(); try { Thread.sleep(delay); @@ -72,19 +73,19 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin }*/ - if(reward == Reward.FOOD_DROP_DOWN_SUCCESS){ + if(reward == Reward.FOOD_DROP_DOWN_SUCCESS) { foodCollected++; foodTimestampsTotal += timestampTilFood; - if(foodCollected % 1000 == 0){ - System.out.println(foodTimestampsTotal / 1000f + " " + timestampCurrentEpisode); - File file = new File(ContinuousAnt.FILE_NAME); + //System.out.println(foodCollected + " " + timestampCurrentEpisode); + File file = new File(ContinuousAnt.FILE_NAME); + if(foodCollected % 1000 == 0) { try { - Files.writeString(Path.of(file.getPath()), foodTimestampsTotal/1000f +",", StandardOpenOption.APPEND); + Files.writeString(Path.of(file.getPath()), timestampCurrentEpisode + ",", StandardOpenOption.APPEND); } catch (IOException e) { e.printStackTrace(); } - foodTimestampsTotal = 0; } + foodTimestampsTotal = 0; if(foodCollected == 1000){ ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.15f); } @@ -98,8 +99,7 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin System.out.println("final 0 expl"); ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.00f); } - if(foodCollected == 15000){ - File file = new File(ContinuousAnt.FILE_NAME); + if(foodCollected == 30000) { try { Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); } catch (IOException e) { diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index e6d89dd..0a75ec2 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -8,31 +8,47 @@ import evironment.antGame.AntWorldContinuous; import java.io.File; import java.io.IOException; +import java.util.ArrayList; +import java.util.List; public class ContinuousAnt { - public static final String FILE_NAME = "converge22.txt"; + public static final String FILE_NAME = "optDiscTimestampsNew.txt"; public static void main(String[] args) { - int i = 4+4+4+6+6+6+8+10+12+14+14+16+16+16+18+18+18+20+20+20+22+22+22+24+24+24+24+26+26+26+26+26+28+28+28+28+28+30+30+30+30+32+32+32+34+34+34+36+36+38+40+42; - System.out.println(i/52f); + int k = 4 + 4 + 4 + 6 + 6 + 6 + 8 + 10 + 12 + 14 + 14 + 16 + 16 + 16 + 18 + 18 + 18 + 20 + 20 + 20 + 22 + 22 + 22 + 24 + 24 + 24 + 24 + 26 + 26 + 26 + 26 + 26 + 28 + 28 + 28 + 28 + 28 + 30 + 30 + 30 + 30 + 32 + 32 + 32 + 34 + 34 + 34 + 36 + 36 + 38 + 40 + 42; + System.out.println(k / 52f); File file = new File(FILE_NAME); try { file.createNewFile(); } catch (IOException e) { e.printStackTrace(); } - RNG.setSeed(56); - RLController rl = new RLController<>( - new AntWorldContinuous(8, 8), - Method.Q_LEARNING_OFF_POLICY_CONTROL, - AntAction.values()); - rl.setDelay(0); - rl.setNrOfEpisodes(1); - //0.99 0.9 0.5 - //0.99 0.95 0.9 0.7 0.5 0.3 0.1 - rl.setDiscountFactor(0.1f); - // 0.1, 0.3, 0.5, 0.7 0.9 - rl.setLearningRate(0.9f); - rl.setEpsilon(0.2f); - rl.start(); + List discValues = new ArrayList<>() { + }; + discValues.add(0.05f); + discValues.add(0.1f); + discValues.add(0.3f); + discValues.add(0.5f); + discValues.add(0.7f); + discValues.add(0.9f); + discValues.add(0.95f); + discValues.add(0.99f); + + for(float disc : discValues) { + RNG.setSeed(13); + RLController rl = new RLController<>( + new AntWorldContinuous(8, 8), + Method.Q_LEARNING_OFF_POLICY_CONTROL, + AntAction.values()); + rl.setDelay(0); + rl.setNrOfEpisodes(1); + //0.99 0.9 0.5 + //0.99 0.95 0.9 0.7 0.5 0.3 0.1 + rl.setDiscountFactor(disc); + // 0.1, 0.3, 0.5, 0.7 0.9 + rl.setLearningRate(0.9f); + rl.setEpsilon(0.2f); + rl.start(); + } + } } From b9be640284645a338ef7676f7edc28cd57d6434b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sun, 5 Apr 2020 12:00:16 +0200 Subject: [PATCH 16/18] add multiple folders to organize results --- SimpleZ1B2QLearning.png | Bin 18871 -> 0 bytes SimpleZ1B2SARSA.png | Bin 18563 -> 0 bytes SimpleZ1B2SarsaA.png | Bin 26617 -> 0 bytes SimpleZ2B1MonteCarlo.png | Bin 18298 -> 0 bytes SimpleZ2B1MonteCarloA.png | Bin 23452 -> 0 bytes SimpleZ2B1QLearning.png | Bin 18791 -> 0 bytes SimpleZ2B1QLearningMaxValueAsDefault.png | Bin 20347 -> 0 bytes SimpleZ2B2QLearning.png | Bin 18467 -> 0 bytes SimpleZ2B2SARSA.png | Bin 18163 -> 0 bytes SimpleZ2B4QLearning.png | Bin 17258 -> 0 bytes epsilonValues.R | 84 ------------------ .../avgEpisodesUntilConvergence.R | 4 +- .../avgRewardPerTimestampDiffLRandDiscQ.R | 2 +- .../avgTimestampsPerFood.R | 6 +- .../totalTimestampsWhileCollecting.R | 3 +- .../antAnalysisAvgReward.txt | 0 .../avgTimestampsPerFoodDiffDiscounts.txt | 2 +- .../convergencSimpleQLearning099disc09LR.txt | 0 .../convergenceAdvancedMC.txt | 2 +- .../convergenceAdvancedMCnegRew.txt | 0 .../convergenceSimpleMC.txt | 0 ...SimpleNegRewInJumpQLearning099disc09LR.txt | 2 +- ...onvergenceSimpleNoJumpInfoMCfirstVisit.txt | 2 +- ...nceSimpleNoJumpingQLearning099disc09LR.txt | 0 ...ergenceSimpleNoJumpingSARSA099disc09LR.txt | 0 ...genceSimpleQ099Disc09LRSameRewardsAsMC.txt | 0 ...c09LRSameRewardsAsMCMinus1DefaultValue.txt | 0 .../convergenceSimpleSARSA099disc09LR.txt | 0 ...alTimestampsNeededForFoodDiffDiscounts.txt | 2 +- .../AdvancedZ3B1MonteCarlo.png | Bin .../AdvancedZ3B3Monte.png | Bin .../SimpleZ1B2QLearningDisc099LR09.png | Bin .../SimpleZ2B1QLearningDisc099LR09.png | Bin ...eZ2B1QLearningMaxInitValuesDisc099LR09.png | Bin .../SimpleZ2B2QLearningDisc099LR09.png | Bin .../SimpleZ2B2SarsaDisc099LR09.png | Bin .../SimpleZ2B4QLearningDisc099LR09.png | Bin .../antGameAnalysis05Disc.png | Bin .../antGameAnalysis099Disc.png | Bin .../antGameAnalysis09Disc.png | Bin .../avgTimestampsPerFoodOptimality.png | Bin .../mininumNeededTimestampsAntGame.png | Bin .../totalTimestampsContinuousCollecting.png | Bin .../algo/td/QLearningOffPolicyTDControl.java | 8 +- src/main/java/example/ContinuousAnt.java | 23 ++--- 45 files changed, 22 insertions(+), 118 deletions(-) delete mode 100644 SimpleZ1B2QLearning.png delete mode 100644 SimpleZ1B2SARSA.png delete mode 100644 SimpleZ1B2SarsaA.png delete mode 100644 SimpleZ2B1MonteCarlo.png delete mode 100644 SimpleZ2B1MonteCarloA.png delete mode 100644 SimpleZ2B1QLearning.png delete mode 100644 SimpleZ2B1QLearningMaxValueAsDefault.png delete mode 100644 SimpleZ2B2QLearning.png delete mode 100644 SimpleZ2B2SARSA.png delete mode 100644 SimpleZ2B4QLearning.png delete mode 100644 epsilonValues.R rename EpsilonAnalysis.R => rScripts/avgEpisodesUntilConvergence.R (92%) rename DiscAndLRonQLearningAdvanced.R => rScripts/avgRewardPerTimestampDiffLRandDiscQ.R (97%) rename OptimalityDifferentDiscountFactors.R => rScripts/avgTimestampsPerFood.R (94%) rename OptimalityDifferentDiscountFactorTotalTS.R => rScripts/totalTimestampsWhileCollecting.R (97%) rename antAnalysisAvgReward.txt => rawResults/antAnalysisAvgReward.txt (100%) rename optDiscNew.txt => rawResults/avgTimestampsPerFoodDiffDiscounts.txt (91%) rename convergencSimpleQLearning099disc09LR.txt => rawResults/convergencSimpleQLearning099disc09LR.txt (100%) rename convergenceAdvanced.txt => rawResults/convergenceAdvancedMC.txt (99%) rename convergenceAdvancedMCnegRew.txt => rawResults/convergenceAdvancedMCnegRew.txt (100%) rename convergenceSimple.txt => rawResults/convergenceSimpleMC.txt (100%) rename convergenceSimpleNegRewInJumpQLearning099disc09LR.txt => rawResults/convergenceSimpleNegRewInJumpQLearning099disc09LR.txt (98%) rename convergenceSimpleNoJumpInfo.txt => rawResults/convergenceSimpleNoJumpInfoMCfirstVisit.txt (96%) rename convergenceSimpleNoJumpingQLearning099disc09LR.txt => rawResults/convergenceSimpleNoJumpingQLearning099disc09LR.txt (100%) rename convergenceSimpleNoJumpingSARSA099disc09LR.txt => rawResults/convergenceSimpleNoJumpingSARSA099disc09LR.txt (100%) rename convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt => rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt (100%) rename convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt => rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt (100%) rename convergenceSimpleSARSA099disc09LR.txt => rawResults/convergenceSimpleSARSA099disc09LR.txt (100%) rename optDiscTimestampsNew.txt => rawResults/totalTimestampsNeededForFoodDiffDiscounts.txt (93%) rename AdvancedZ3B1MonteCarloA.png => resultDiagrams/AdvancedZ3B1MonteCarlo.png (100%) rename AdvancedZ3B3MonteA.png => resultDiagrams/AdvancedZ3B3Monte.png (100%) rename SimpleZ1B2QLearningA.png => resultDiagrams/SimpleZ1B2QLearningDisc099LR09.png (100%) rename SimpleZ2B1QLearningA.png => resultDiagrams/SimpleZ2B1QLearningDisc099LR09.png (100%) rename SimpleZ2B1QLearningMaxA.png => resultDiagrams/SimpleZ2B1QLearningMaxInitValuesDisc099LR09.png (100%) rename SimpleZ2B2QLearningA.png => resultDiagrams/SimpleZ2B2QLearningDisc099LR09.png (100%) rename SimpleZ2B2SarsaA.png => resultDiagrams/SimpleZ2B2SarsaDisc099LR09.png (100%) rename SimpleZ2B4QLearningA.png => resultDiagrams/SimpleZ2B4QLearningDisc099LR09.png (100%) rename antGameAnalysis05DiscA.png => resultDiagrams/antGameAnalysis05Disc.png (100%) rename antGameAnalysis099DiscA.png => resultDiagrams/antGameAnalysis099Disc.png (100%) rename antGameAnalysis09DiscA.png => resultDiagrams/antGameAnalysis09Disc.png (100%) rename optDisc.png => resultDiagrams/avgTimestampsPerFoodOptimality.png (100%) rename NeededTimestampsFromEverywhere.png => resultDiagrams/mininumNeededTimestampsAntGame.png (100%) rename optDiscTotalTS.png => resultDiagrams/totalTimestampsContinuousCollecting.png (100%) diff --git 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Create dummy data -data <- data.frame( - y=ta[,1], - y2=ta[,2], - y3=ta[,3], - y4=ta[,4], - y5=ta[,5], - y6=ta[,6], - y7=ta[,7], - y8=ta[,8], - y9=ta[,9], - y10=ta[,10], - y11=ta[,11], - y12=ta[,12], - y13=ta[,13], - y14=ta[,14], - y15=ta[,15], - x=seq(1, length(ta[,1])) -) -ggplot(data, aes(x*1000)) + - labs(title="Discount factor = 0.99", - x ="Timestamp", y = "Avg. reward per timestamp", color = "Learning rate") + - ylim(-1.5,0.6) + - geom_line(aes(y = y, colour = "0.1"), size=1)+ - geom_line(aes(y = y2, colour = "0.3"), size=1) + - geom_line(aes(y = y3, colour = "0.5"), size=1) + - geom_line(aes(y = y4, colour = "0.7"), size=1) + - geom_line(aes(y = y5, colour = "0.9"), size=1) - -ggplot(data, aes(x*1000)) + - labs(title="Discount factor = 0.9", - x ="Timestamp", y = "Avg. reward per timestamp", color = "Learning rate") + - ylim(-1.5,0.6) + - geom_line(aes(y = y6, colour = "0.1"), size=1) + - geom_line(aes(y = y7, colour = "0.3"), size=1) + - geom_line(aes(y = y8, colour = "0.5"), size=1) + - geom_line(aes(y = y9, colour = "0.7"), size=1) + - geom_line(aes(y = y10, colour = "0.9"), size=1) - -ggplot(data, aes(x*1000) ) + - labs(title="Discount factor = 0.5", - x ="Timestamp", y = "Avg. reward per timestamp", color = "Learning rate") + - ylim(-1.5,0.6) + - geom_line(aes(y = y11, colour = "0.1"), size=1) + - geom_line(aes(y = y12, colour = "0.3"), size=1) + - geom_line(aes(y = y13, colour = "0.5"), size=1) + - geom_line(aes(y = y14, colour = "0.7"), size=1) + - geom_line(aes(y = y15, colour = "0.9"), size=1) - - # scale_x_log10(limits=c(1,150) ) - # scale_y_log10( breaks=c(1,50,500,2500,25000), limits=c(1,25000) ) - -plot(ta, x=x*1000, log="x", type="o") - -convergence <- read.csv(file.choose(), header=FALSE, row.names=1) - -sds <- rowSds(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) -men <- rowMeans(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) -print(sds) - -# create dummy data -data <- data.frame( - names=rownames(convergence), - means=men, - sds=sds -) - -ggplot(data) + - geom_bar(aes(x=names, y=means), stat="identity", fill="skyblue", alpha=0.7) + - geom_errorbar( aes(x=names, ymin=means-sds, ymax=means+sds), width=0.4, colour="orange", alpha=0.9, size=1.3) + - geom_text(aes(label=as.integer(means), x =names, y=means), position=position_dodge(width=0.9), vjust=-0.25) + - xlab("Epsilon") + ylab("avg. amount of episodes until convergence") - - -ba <- barplot(names=rownames(convergence), height=men, ylim=c(0, max(men)*1.2), ylab = "avg. episodes until convergence", xlab = "epsilon value") -text(x = ba, y = men, label = as.integer(men), pos = 3, cex = 0.8, col = "red") diff --git a/EpsilonAnalysis.R b/rScripts/avgEpisodesUntilConvergence.R similarity index 92% rename from EpsilonAnalysis.R rename to rScripts/avgEpisodesUntilConvergence.R index 2e260ab..2c75632 100644 --- a/EpsilonAnalysis.R +++ b/rScripts/avgEpisodesUntilConvergence.R @@ -7,7 +7,7 @@ sds <- rowSds(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]) men <- rowMeans(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) print(sds) -# create dummy data +# create data frame data <- data.frame( names=rownames(convergence), means=men, @@ -20,6 +20,6 @@ convergence.m <- melt(convergence, id.vars = "groups") ggplot(data) + geom_bar(aes(x=names, y=means, fill=means), stat="identity", colour="black", alpha=0.8) + geom_errorbar( aes(x=names, ymin=means, ymax=means+sds), width=0.4, colour="black", alpha=0.8, size=0.6) + - ylim(0,104000) + + # ylim(0,104000) + xlab("Epsilon") + ylab("Ø Episoden bis Konvergenz") + theme_bw(base_size = 24) \ No newline at end of file diff --git a/DiscAndLRonQLearningAdvanced.R b/rScripts/avgRewardPerTimestampDiffLRandDiscQ.R similarity index 97% rename from DiscAndLRonQLearningAdvanced.R rename to rScripts/avgRewardPerTimestampDiffLRandDiscQ.R index f840762..6ee30c3 100644 --- a/DiscAndLRonQLearningAdvanced.R +++ b/rScripts/avgRewardPerTimestampDiffLRandDiscQ.R @@ -6,7 +6,7 @@ ta <- t(ta) dim(ta) head(ta) -# Create dummy data +# Create data frame data <- data.frame( y=ta[,1], y2=ta[,2], diff --git a/OptimalityDifferentDiscountFactors.R b/rScripts/avgTimestampsPerFood.R similarity index 94% rename from OptimalityDifferentDiscountFactors.R rename to rScripts/avgTimestampsPerFood.R index 6a1f58a..4d09818 100644 --- a/OptimalityDifferentDiscountFactors.R +++ b/rScripts/avgTimestampsPerFood.R @@ -5,8 +5,8 @@ ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1)) ta <- t(ta) dim(ta) head(ta) -print(ta[1:20,1]) -# Create dummy data + +# Create data frame data <- data.frame( y=ta[1:20,1], y2=ta[1:20,2], @@ -20,7 +20,7 @@ data <- data.frame( ) ggplot(data, aes(x*1000)) + labs( x ="Gesamtanzahl gesammeltes Futter", y = "Ø Zeitstempel pro Futter (log)", color = "Diskontierungsfaktor") + - scale_y_log10()+ + #scale_y_log10()+ geom_hline(yintercept=23, linetype="dashed")+ geom_text(aes(0,23,label = "opt", vjust = -1)) + geom_line(aes(y = y, colour = "0.05"), size=1)+ diff --git a/OptimalityDifferentDiscountFactorTotalTS.R b/rScripts/totalTimestampsWhileCollecting.R similarity index 97% rename from OptimalityDifferentDiscountFactorTotalTS.R rename to rScripts/totalTimestampsWhileCollecting.R index 7af1295..1d34138 100644 --- a/OptimalityDifferentDiscountFactorTotalTS.R +++ b/rScripts/totalTimestampsWhileCollecting.R @@ -5,7 +5,8 @@ ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1)) ta <- t(ta) dim(ta) head(ta) -# Create dummy data + +# Create data frame data <- data.frame( y=ta[,1], y2=ta[,2], diff --git a/antAnalysisAvgReward.txt b/rawResults/antAnalysisAvgReward.txt similarity index 100% rename from antAnalysisAvgReward.txt rename to rawResults/antAnalysisAvgReward.txt diff --git a/optDiscNew.txt b/rawResults/avgTimestampsPerFoodDiffDiscounts.txt similarity index 91% rename from optDiscNew.txt rename to rawResults/avgTimestampsPerFoodDiffDiscounts.txt index 40f9006..7b7ecd1 100644 --- a/optDiscNew.txt +++ b/rawResults/avgTimestampsPerFoodDiffDiscounts.txt @@ -1,4 +1,4 @@ -# 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99 +# Average Timestamps needed to collect one food piece, mean over 1000 collected food; Discount factor for rows: 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99 103.857,48.501,49.283,44.149,44.51,42.402,45.102,41.896,43.104,40.76,41.0,39.134,46.756,44.282,45.834,44.942,41.078,43.66,42.812,41.672,44.022,41.196,42.106,41.384,42.174,42.736,45.792,43.482,48.132,44.14 86.807,32.319,29.317,26.51,25.664,24.348,24.994,23.972,24.46,24.252,24.128,23.758,25.184,24.872,25.286,24.356,24.322,24.262,24.26,24.446,24.888,24.302,24.866,24.236,24.548,24.866,25.894,24.872,26.212,24.632 81.071,30.951,26.07,24.804,23.196,22.67,23.374,22.388,22.666,22.776,22.432,22.294,23.318,23.018,23.44,22.97,22.502,22.776,22.692,22.382,23.178,22.932,23.098,22.726,22.584,23.062,23.548,22.824,23.672,22.73 diff --git a/convergencSimpleQLearning099disc09LR.txt b/rawResults/convergencSimpleQLearning099disc09LR.txt similarity index 100% rename from convergencSimpleQLearning099disc09LR.txt rename to rawResults/convergencSimpleQLearning099disc09LR.txt diff --git a/convergenceAdvanced.txt b/rawResults/convergenceAdvancedMC.txt similarity index 99% rename from convergenceAdvanced.txt rename to rawResults/convergenceAdvancedMC.txt index 9099183..4ecea2c 100644 --- a/convergenceAdvanced.txt +++ b/rawResults/convergenceAdvancedMC.txt @@ -1,4 +1,4 @@ -# 4 speeds, 4 distance. Distance, inJump and incomming speed; Monte Carlo +# 4 speeds, 4 distance. Distance, inJump and incoming speed; Monte Carlo 0.05,8565,3803,4010,30744,5419,1733,30335,4070,7502,7880,12776,2242,10489,4384,23578,14718,17801,7770,4097,5905,4027,5969,8492,1994,1542,18577,2346,13550,2279,5072,39861,15109,4419,10970,6150,2359,3093,7131,5932,18531,6887,22726,4925,3320,6832,13704,10372,25447,22945,28991,4526,12461,1831,20964,21963,2749,7112,3260,33941,14907,9168,7133,6590,21075,5302,2546,10164,3101,3204,7850,40776,38750,32898,14204,4431,12038,13430,13976,7385,7647,9634,12230,6635,9532,4181,12857,10473,2609,3827,4800,4316,5114,4153,6282,4737,5078,7358,11660,4750,16256 0.1,1550,4416,3473,2821,2101,3635,5168,9641,7074,5623,2571,2253,2595,4347,24575,4239,4552,6372,6638,2686,33218,1976,7638,2479,3555,4496,2321,11712,1934,3668,9398,1312,4422,3217,9472,7053,5539,2140,2928,1576,12121,5023,2458,5947,2374,10197,1701,6186,7013,3061,3108,1856,4052,2327,2287,2026,1606,1567,2414,3756,2648,3409,4195,4803,11940,2171,1574,2656,3221,2044,2250,1528,1348,7785,3141,6622,25696,4520,2118,2489,7125,4182,1495,2475,5037,3839,4218,1929,2302,5339,1827,5444,3710,47322,2555,2373,2108,1550,4416,3473 0.15,2328,3189,5422,1943,2057,1630,1405,2079,8071,22741,4056,1704,2727,2339,10381,1246,1831,6658,2760,9416,4036,1243,3716,1199,8678,5480,4038,42601,1133,3576,3852,4014,1487,5849,4275,3766,2240,3917,2164,3695,1954,1869,1854,1751,2785,2334,2821,5942,1310,1680,7867,1987,2345,1785,3898,1598,4382,2881,4874,2345,2483,3962,8058,10204,6444,2885,8494,5135,7150,1041,8020,24960,1720,45430,3546,3751,2077,2269,6051,5372,4247,10739,7114,1169,1757,1162,2699,1765,1367,3226,2290,3695,1756,2452,7385,2599,1426,7600,15285,4712 diff --git a/convergenceAdvancedMCnegRew.txt b/rawResults/convergenceAdvancedMCnegRew.txt similarity index 100% rename from convergenceAdvancedMCnegRew.txt rename to rawResults/convergenceAdvancedMCnegRew.txt diff --git a/convergenceSimple.txt b/rawResults/convergenceSimpleMC.txt similarity index 100% rename from convergenceSimple.txt rename to rawResults/convergenceSimpleMC.txt diff --git a/convergenceSimpleNegRewInJumpQLearning099disc09LR.txt b/rawResults/convergenceSimpleNegRewInJumpQLearning099disc09LR.txt similarity index 98% rename from convergenceSimpleNegRewInJumpQLearning099disc09LR.txt rename to rawResults/convergenceSimpleNegRewInJumpQLearning099disc09LR.txt index e8c8644..aca0ba4 100644 --- a/convergenceSimpleNegRewInJumpQLearning099disc09LR.txt +++ b/rawResults/convergenceSimpleNegRewInJumpQLearning099disc09LR.txt @@ -1,4 +1,4 @@ -# Simple, Dist and inJump, QLearning 0.99 disc 0.9 Learning rade, -1 reward when in jump and jumping +# Simple, Dist and inJump, QLearning 0.99 disc 0.9 Learning rate, -1 reward when in jump and jumping 0.05,6,6,10,8,8,7,6,10,8,6,8,7,8,6,10,10,9,9,12,7,7,8,9,11,8,12,7,8,8,8,9,8,10,7,10,8,9,9,7,7,8,7,7,5,11,8,8,7,8,7,8,8,7,7,9,8,6,5,10,10,7,10,8,7,7,7,7,11,8,9,10,8,10,8,7,6,5,8,7,8,8,8,3,8,9,9,4,8,6,11,7,8,8,8,9,8,12,7,10,9 0.1,6,6,8,10,8,7,6,7,9,6,8,10,8,7,10,22,7,11,9,9,8,14,8,29,8,10,7,7,9,7,10,8,11,7,9,9,10,7,7,14,7,7,10,9,5,6,10,8,9,10,8,6,10,9,10,8,6,18,9,32,6,10,8,7,8,7,7,5,8,9,8,8,8,8,6,6,5,9,7,6,8,8,6,12,9,9,4,8,6,11,8,8,9,7,8,7,10,8,12,37 0.15,7,6,5,9,7,7,6,6,8,15,7,7,13,8,8,16,7,16,12,12,8,21,7,11,7,26,4,7,10,7,10,8,7,7,12,10,6,10,6,6,6,15,20,9,5,8,8,7,10,10,7,48,10,8,12,7,7,6,8,11,6,10,9,7,8,35,8,5,10,5,14,14,10,8,12,8,7,7,7,8,8,7,7,20,9,13,4,10,6,26,12,8,8,10,7,8,11,63,13,16 diff --git a/convergenceSimpleNoJumpInfo.txt b/rawResults/convergenceSimpleNoJumpInfoMCfirstVisit.txt similarity index 96% rename from convergenceSimpleNoJumpInfo.txt rename to rawResults/convergenceSimpleNoJumpInfoMCfirstVisit.txt index c4e74ce..cf26515 100644 --- a/convergenceSimpleNoJumpInfo.txt +++ b/rawResults/convergenceSimpleNoJumpInfoMCfirstVisit.txt @@ -1,4 +1,4 @@ -#same speed and distance. ONLY DISTANCE TO OBSTACLE. 100 trials, rest did not convergec; MONTE CARLO +#same speed and distance. ONLY DISTANCE TO OBSTACLE. 100 trials, rest did not converged!; MONTE CARLO 0.05,8,64,62,15,84,5,72,183 0.1,15,15,6,18,46,28,307 0.15,177,20,12,9,21,4,6,65,173 diff --git a/convergenceSimpleNoJumpingQLearning099disc09LR.txt b/rawResults/convergenceSimpleNoJumpingQLearning099disc09LR.txt similarity index 100% rename from convergenceSimpleNoJumpingQLearning099disc09LR.txt rename to rawResults/convergenceSimpleNoJumpingQLearning099disc09LR.txt diff --git a/convergenceSimpleNoJumpingSARSA099disc09LR.txt b/rawResults/convergenceSimpleNoJumpingSARSA099disc09LR.txt similarity index 100% rename from convergenceSimpleNoJumpingSARSA099disc09LR.txt rename to rawResults/convergenceSimpleNoJumpingSARSA099disc09LR.txt diff --git a/convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt b/rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt similarity index 100% rename from convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt rename to rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt diff --git a/convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt b/rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt similarity index 100% rename from convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt rename to rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt diff --git a/convergenceSimpleSARSA099disc09LR.txt b/rawResults/convergenceSimpleSARSA099disc09LR.txt similarity index 100% rename from convergenceSimpleSARSA099disc09LR.txt rename to rawResults/convergenceSimpleSARSA099disc09LR.txt diff --git a/optDiscTimestampsNew.txt b/rawResults/totalTimestampsNeededForFoodDiffDiscounts.txt similarity index 93% rename from optDiscTimestampsNew.txt rename to rawResults/totalTimestampsNeededForFoodDiffDiscounts.txt index 7b3ec46..dda8f33 100644 --- a/optDiscTimestampsNew.txt +++ b/rawResults/totalTimestampsNeededForFoodDiffDiscounts.txt @@ -1,4 +1,4 @@ -# timestamp needed for x*1000 collected food; 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99 +# timestamp needed for x*1000 collected food; Discount factor for rows: 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99 103856,152357,201640,245789,290299,332701,377803,419699,462803,503563,544563,583697,630453,674735,720569,765511,806589,850249,893061,934733,978755,1019951,1062057,1103441,1145615,1188351,1234143,1277625,1325757,1369897 86806,119125,148442,174952,200616,224964,249958,273930,298390,322642,346770,370528,395712,420584,445870,470226,494548,518810,543070,567516,592404,616706,641572,665808,690356,715222,741116,765988,792200,816832 81070,112021,138091,162895,186091,208761,232135,254523,277189,299965,322397,344691,368009,391027,414467,437437,459939,482715,505407,527789,550967,573899,596997,619723,642307,665369,688917,711741,735413,758143 diff --git a/AdvancedZ3B1MonteCarloA.png b/resultDiagrams/AdvancedZ3B1MonteCarlo.png similarity index 100% rename from AdvancedZ3B1MonteCarloA.png rename to resultDiagrams/AdvancedZ3B1MonteCarlo.png diff --git a/AdvancedZ3B3MonteA.png b/resultDiagrams/AdvancedZ3B3Monte.png similarity index 100% rename from AdvancedZ3B3MonteA.png rename to resultDiagrams/AdvancedZ3B3Monte.png diff --git a/SimpleZ1B2QLearningA.png b/resultDiagrams/SimpleZ1B2QLearningDisc099LR09.png similarity index 100% rename from SimpleZ1B2QLearningA.png rename to resultDiagrams/SimpleZ1B2QLearningDisc099LR09.png diff --git a/SimpleZ2B1QLearningA.png b/resultDiagrams/SimpleZ2B1QLearningDisc099LR09.png similarity index 100% rename from SimpleZ2B1QLearningA.png rename to resultDiagrams/SimpleZ2B1QLearningDisc099LR09.png diff --git a/SimpleZ2B1QLearningMaxA.png b/resultDiagrams/SimpleZ2B1QLearningMaxInitValuesDisc099LR09.png similarity index 100% rename from SimpleZ2B1QLearningMaxA.png rename to resultDiagrams/SimpleZ2B1QLearningMaxInitValuesDisc099LR09.png diff --git a/SimpleZ2B2QLearningA.png b/resultDiagrams/SimpleZ2B2QLearningDisc099LR09.png similarity index 100% rename from SimpleZ2B2QLearningA.png rename to resultDiagrams/SimpleZ2B2QLearningDisc099LR09.png diff --git a/SimpleZ2B2SarsaA.png b/resultDiagrams/SimpleZ2B2SarsaDisc099LR09.png similarity index 100% rename from SimpleZ2B2SarsaA.png rename to resultDiagrams/SimpleZ2B2SarsaDisc099LR09.png diff --git a/SimpleZ2B4QLearningA.png b/resultDiagrams/SimpleZ2B4QLearningDisc099LR09.png similarity index 100% rename from SimpleZ2B4QLearningA.png rename to resultDiagrams/SimpleZ2B4QLearningDisc099LR09.png diff --git a/antGameAnalysis05DiscA.png b/resultDiagrams/antGameAnalysis05Disc.png similarity index 100% rename from antGameAnalysis05DiscA.png rename to resultDiagrams/antGameAnalysis05Disc.png diff --git a/antGameAnalysis099DiscA.png b/resultDiagrams/antGameAnalysis099Disc.png similarity index 100% rename from antGameAnalysis099DiscA.png rename to resultDiagrams/antGameAnalysis099Disc.png diff --git a/antGameAnalysis09DiscA.png b/resultDiagrams/antGameAnalysis09Disc.png similarity index 100% rename from antGameAnalysis09DiscA.png rename to resultDiagrams/antGameAnalysis09Disc.png diff --git a/optDisc.png b/resultDiagrams/avgTimestampsPerFoodOptimality.png similarity index 100% rename from optDisc.png rename to resultDiagrams/avgTimestampsPerFoodOptimality.png diff --git a/NeededTimestampsFromEverywhere.png b/resultDiagrams/mininumNeededTimestampsAntGame.png similarity index 100% rename from NeededTimestampsFromEverywhere.png rename to resultDiagrams/mininumNeededTimestampsAntGame.png diff --git a/optDiscTotalTS.png b/resultDiagrams/totalTimestampsContinuousCollecting.png similarity index 100% rename from optDiscTotalTS.png rename to resultDiagrams/totalTimestampsContinuousCollecting.png diff --git a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java index 79ba81f..470b60f 100644 --- a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java +++ b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java @@ -76,16 +76,16 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin if(reward == Reward.FOOD_DROP_DOWN_SUCCESS) { foodCollected++; foodTimestampsTotal += timestampTilFood; - //System.out.println(foodCollected + " " + timestampCurrentEpisode); File file = new File(ContinuousAnt.FILE_NAME); if(foodCollected % 1000 == 0) { + System.out.println(foodTimestampsTotal / 1000f + " " + timestampCurrentEpisode); try { - Files.writeString(Path.of(file.getPath()), timestampCurrentEpisode + ",", StandardOpenOption.APPEND); + Files.writeString(Path.of(file.getPath()), foodTimestampsTotal / 1000f + ",", StandardOpenOption.APPEND); } catch (IOException e) { e.printStackTrace(); } + foodTimestampsTotal = 0; } - foodTimestampsTotal = 0; if(foodCollected == 1000){ ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.15f); } @@ -105,7 +105,7 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin } catch (IOException e) { e.printStackTrace(); } - return; + // return; } iterations++; timestampTilFood = 0; diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index 0a75ec2..1e7592e 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -3,13 +3,12 @@ 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; -import java.util.ArrayList; -import java.util.List; public class ContinuousAnt { public static final String FILE_NAME = "optDiscTimestampsNew.txt"; @@ -22,33 +21,21 @@ public class ContinuousAnt { } catch (IOException e) { e.printStackTrace(); } - List discValues = new ArrayList<>() { - }; - discValues.add(0.05f); - discValues.add(0.1f); - discValues.add(0.3f); - discValues.add(0.5f); - discValues.add(0.7f); - discValues.add(0.9f); - discValues.add(0.95f); - discValues.add(0.99f); - - for(float disc : discValues) { RNG.setSeed(13); - RLController rl = new RLController<>( + RLController rl = new RLControllerGUI<>( new AntWorldContinuous(8, 8), Method.Q_LEARNING_OFF_POLICY_CONTROL, AntAction.values()); - rl.setDelay(0); + rl.setDelay(20); rl.setNrOfEpisodes(1); //0.99 0.9 0.5 //0.99 0.95 0.9 0.7 0.5 0.3 0.1 - rl.setDiscountFactor(disc); + rl.setDiscountFactor(0.05f); // 0.1, 0.3, 0.5, 0.7 0.9 rl.setLearningRate(0.9f); rl.setEpsilon(0.2f); rl.start(); - } + } } From 5b82e7965da93c5d062bff60cb98b809fa87105f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sun, 5 Apr 2020 12:29:44 +0200 Subject: [PATCH 17/18] rename MC class and improve specific analysis of antGame examples --- ...edy.java => MonteCarloControlEGreedy.java} | 10 ++-- .../algo/td/QLearningOffPolicyTDControl.java | 17 +----- .../java/core/controller/RLController.java | 6 +-- src/main/java/example/ContinuousAnt.java | 26 +++++----- src/main/java/example/DinoSampling.java | 11 ++-- src/main/java/example/Results | 15 ------ src/main/java/example/RunningAnt.java | 2 +- src/main/java/example/Test.java | 52 ------------------- 8 files changed, 28 insertions(+), 111 deletions(-) rename src/main/java/core/algo/mc/{MonteCarloControlFirstVisitEGreedy.java => MonteCarloControlEGreedy.java} (88%) delete mode 100644 src/main/java/example/Results delete mode 100644 src/main/java/example/Test.java diff --git a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java b/src/main/java/core/algo/mc/MonteCarloControlEGreedy.java similarity index 88% rename from src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java rename to src/main/java/core/algo/mc/MonteCarloControlEGreedy.java index f00fa4b..c0f78d8 100644 --- a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java +++ b/src/main/java/core/algo/mc/MonteCarloControlEGreedy.java @@ -12,19 +12,19 @@ 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 */ -public class MonteCarloControlFirstVisitEGreedy extends EpisodicLearning { +public class MonteCarloControlEGreedy extends EpisodicLearning { private Map, Double> returnSum; private Map, Integer> returnCount; private boolean isEveryVisit; - public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay, boolean useEveryVisit) { + public MonteCarloControlEGreedy(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay, boolean useEveryVisit) { super(environment, actionSpace, discountFactor, delay); isEveryVisit = useEveryVisit; this.policy = new EpsilonGreedyPolicy<>(epsilon); @@ -33,11 +33,11 @@ public class MonteCarloControlFirstVisitEGreedy extends Episodic returnCount = new HashMap<>(); } - public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay) { + public MonteCarloControlEGreedy(Environment environment, DiscreteActionSpace actionSpace, float discountFactor, float epsilon, int delay) { this(environment, actionSpace, discountFactor, epsilon, delay, false); } - public MonteCarloControlFirstVisitEGreedy(Environment environment, DiscreteActionSpace actionSpace, int delay) { + public MonteCarloControlEGreedy(Environment environment, DiscreteActionSpace actionSpace, int delay) { this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, delay); } diff --git a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java index fe0c5cf..2da1c60 100644 --- a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java +++ b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java @@ -45,9 +45,7 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin sumOfRewards = 0; int timestampTilFood = 0; - int rewardsPer1000 = 0; int foodCollected = 0; - int iterations = 0; int foodTimestampsTotal= 0; while(envResult == null || !envResult.isDone()) { actionValues = stateActionTable.getActionValues(state); @@ -58,20 +56,8 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin double reward = envResult.getReward(); State nextState = envResult.getState(); sumOfRewards += reward; - rewardsPer1000+=reward; timestampTilFood++; - /* if(iterations == 100){ - File file = new File(ContinuousAnt.FILE_NAME); - try { - Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); - } catch (IOException e) { - e.printStackTrace(); - } - return; - }*/ - - if(reward == Reward.FOOD_DROP_DOWN_SUCCESS) { foodCollected++; foodTimestampsTotal += timestampTilFood; @@ -95,7 +81,7 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.05f); } if(foodCollected == 4000){ - System.out.println("final 0 expl"); + System.out.println("Reached 0 exploration"); ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.00f); } if(foodCollected == 15000){ @@ -106,7 +92,6 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin } return; } - iterations++; timestampTilFood = 0; } diff --git a/src/main/java/core/controller/RLController.java b/src/main/java/core/controller/RLController.java index 895102a..e4df7e0 100644 --- a/src/main/java/core/controller/RLController.java +++ b/src/main/java/core/controller/RLController.java @@ -7,7 +7,7 @@ import core.ListDiscreteActionSpace; import core.algo.EpisodicLearning; import core.algo.Learning; import core.algo.Method; -import core.algo.mc.MonteCarloControlFirstVisitEGreedy; +import core.algo.mc.MonteCarloControlEGreedy; import core.algo.td.QLearningOffPolicyTDControl; import core.algo.td.SARSA; import core.listener.LearningListener; @@ -49,10 +49,10 @@ public class RLController implements LearningListener { public void start() { switch(method) { case MC_CONTROL_FIRST_VISIT: - learning = new MonteCarloControlFirstVisitEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay); + learning = new MonteCarloControlEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay); break; case MC_CONTROL_EVERY_VISIT: - learning = new MonteCarloControlFirstVisitEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay, true); + learning = new MonteCarloControlEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay, true); break; case SARSA_ON_POLICY_CONTROL: diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index 7a49b1d..e9d7ace 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -11,30 +11,28 @@ import java.io.File; import java.io.IOException; public class ContinuousAnt { - public static final String FILE_NAME = "converge22.txt"; + public static final String FILE_NAME = "converge.txt"; + public static void main(String[] args) { - int i = 4+4+4+6+6+6+8+10+12+14+14+16+16+16+18+18+18+20+20+20+22+22+22+24+24+24+24+26+26+26+26+26+28+28+28+28+28+30+30+30+30+32+32+32+34+34+34+36+36+38+40+42; - System.out.println(i/52f); File file = new File(FILE_NAME); try { file.createNewFile(); } catch (IOException e) { e.printStackTrace(); } - RNG.setSeed(13); + RNG.setSeed(13); RLController rl = new RLControllerGUI<>( - new AntWorldContinuous(8, 8), - Method.Q_LEARNING_OFF_POLICY_CONTROL, - AntAction.values()); + new AntWorldContinuous(8, 8), + Method.Q_LEARNING_OFF_POLICY_CONTROL, + AntAction.values()); rl.setDelay(20); - rl.setNrOfEpisodes(1); - //0.99 0.9 0.5 - //0.99 0.95 0.9 0.7 0.5 0.3 0.1 + 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(); + // 0.1, 0.3, 0.5, 0.7 0.9 + rl.setLearningRate(0.9f); + rl.setEpsilon(0.2f); + rl.start(); } diff --git a/src/main/java/example/DinoSampling.java b/src/main/java/example/DinoSampling.java index c4bbde7..6c748ac 100644 --- a/src/main/java/example/DinoSampling.java +++ b/src/main/java/example/DinoSampling.java @@ -14,8 +14,8 @@ import java.nio.file.Path; import java.nio.file.StandardOpenOption; public class DinoSampling { - public static final float f =0.05f; public static final String FILE_NAME = "converge.txt"; + public static void main(String[] args) { File file = new File(FILE_NAME); try { @@ -23,15 +23,16 @@ public class DinoSampling { } catch (IOException e) { e.printStackTrace(); } - for(float f = 0.05f; f <=1.003 ; f+=0.05f) { + 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); + for(int i = 1; i <= 100; i++) { + int seed = i * 13; + System.out.println("seed: " + seed); + RNG.setSeed(seed); RLController rl = new RLControllerGUI<>( new DinoWorld(), diff --git a/src/main/java/example/Results b/src/main/java/example/Results deleted file mode 100644 index ba42fc7..0000000 --- a/src/main/java/example/Results +++ /dev/null @@ -1,15 +0,0 @@ -Method: -Epsilon = k / currentEpisode -set to 0 if Epsilon < b - -k = 1.5 -b = 0.1 => conv. 16 - -k = 1.5 -b = 0.02 => 75 - -k = 1.4 -b = 0.02 => fail - -k = 2.0 -b = 0.02 => conv. 100 diff --git a/src/main/java/example/RunningAnt.java b/src/main/java/example/RunningAnt.java index d01461c..eddea17 100644 --- a/src/main/java/example/RunningAnt.java +++ b/src/main/java/example/RunningAnt.java @@ -19,8 +19,8 @@ public class RunningAnt { rl.setDelay(200); rl.setNrOfEpisodes(10000); rl.setDiscountFactor(0.9f); + rl.setLearningRate(0.9f); rl.setEpsilon(0.15f); - rl.start(); } } diff --git a/src/main/java/example/Test.java b/src/main/java/example/Test.java deleted file mode 100644 index c7ee691..0000000 --- a/src/main/java/example/Test.java +++ /dev/null @@ -1,52 +0,0 @@ -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"); - } - } -} From ad07c1da8fb73911f43ee15a2de77c1175fe995f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sun, 5 Apr 2020 13:10:13 +0200 Subject: [PATCH 18/18] remove DinoSampling stuff --- src/main/java/core/RNG.java | 14 +++-- src/main/java/core/algo/EpisodicLearning.java | 28 ---------- .../evironment/blackjack/cards/CardDeck.java | 2 +- src/main/java/example/ContinuousAnt.java | 4 +- src/main/java/example/DinoSampling.java | 55 ------------------- 5 files changed, 12 insertions(+), 91 deletions(-) delete mode 100644 src/main/java/example/DinoSampling.java diff --git a/src/main/java/core/RNG.java b/src/main/java/core/RNG.java index 22e9eeb..e506167 100644 --- a/src/main/java/core/RNG.java +++ b/src/main/java/core/RNG.java @@ -20,9 +20,8 @@ public class RNG { private static int envSeed = 13; static { rng = new Random(); - rng.setSeed(seed); rngEnv = new Random(); - rngEnv.setSeed(13); + setSeed(seed, true); } public static Random getRandom() { @@ -32,9 +31,16 @@ public class RNG { return rngEnv; } - public static void setSeed(int seed){ + public static void setSeed(int seed, boolean setEnvRandom) { RNG.seed = seed; rng.setSeed(seed); - rngEnv.setSeed(13); + if(setEnvRandom) { + rngEnv.setSeed(13); + } } + + public static void setSeed(int seed) { + setSeed(seed, true); + } + } diff --git a/src/main/java/core/algo/EpisodicLearning.java b/src/main/java/core/algo/EpisodicLearning.java index 6e57943..885b6d0 100644 --- a/src/main/java/core/algo/EpisodicLearning.java +++ b/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; @@ -80,18 +75,6 @@ public abstract class EpisodicLearning extends Learning imple private void dispatchEpisodeStart(){ ++currentEpisode; - /* - 2f 0.02 => 100 - 1.5f 0.02 => 75 - 1.4f 0.02 => fail - 1.5f 0.1 => 16 ! - */ -// if(this.policy instanceof EpsilonGreedyPolicy){ -// float ep = 2f/(float)currentEpisode; -// if(ep < 0.02) ep = 0; -// ((EpsilonGreedyPolicy) this.policy).setEpsilon(ep); -// System.out.println(ep); -// } episodesToLearn.decrementAndGet(); for(LearningListener l: learningListeners){ l.onEpisodeStart(); @@ -103,17 +86,6 @@ public abstract class EpisodicLearning extends Learning imple super.dispatchStepEnd(); timestamp++; timestampCurrentEpisode++; - // TODO: more sophisticated way to check convergence - if(false){ - // 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!"); - } } @Override diff --git a/src/main/java/evironment/blackjack/cards/CardDeck.java b/src/main/java/evironment/blackjack/cards/CardDeck.java index 4878b62..4db62c4 100644 --- a/src/main/java/evironment/blackjack/cards/CardDeck.java +++ b/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.getRandom().nextInt(cards.size())); + return cards.get(RNG.getRandomEnv().nextInt(cards.size())); } } diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index e9d7ace..5c1df45 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -20,7 +20,7 @@ public class ContinuousAnt { } catch (IOException e) { e.printStackTrace(); } - RNG.setSeed(13); + RNG.setSeed(13, true); RLController rl = new RLControllerGUI<>( new AntWorldContinuous(8, 8), Method.Q_LEARNING_OFF_POLICY_CONTROL, @@ -33,7 +33,5 @@ public class ContinuousAnt { rl.setLearningRate(0.9f); rl.setEpsilon(0.2f); rl.start(); - - } } diff --git a/src/main/java/example/DinoSampling.java b/src/main/java/example/DinoSampling.java deleted file mode 100644 index 6c748ac..0000000 --- a/src/main/java/example/DinoSampling.java +++ /dev/null @@ -1,55 +0,0 @@ -package example; - -import core.RNG; -import core.algo.Method; -import core.controller.RLController; -import core.controller.RLControllerGUI; -import evironment.jumpingDino.DinoAction; -import evironment.jumpingDino.DinoWorld; - -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 = "converge.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++) { - int seed = i * 13; - System.out.println("seed: " + seed); - RNG.setSeed(seed); - - RLController rl = new RLControllerGUI<>( - new DinoWorld(), - Method.MC_CONTROL_FIRST_VISIT, - DinoAction.values()); - rl.setDelay(300); - rl.setDiscountFactor(1f); - rl.setEpsilon(0.5f); - rl.setLearningRate(0.9f); - rl.setNrOfEpisodes(400000); - rl.start(); - } - try { - Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); - } catch (IOException e) { - e.printStackTrace(); - } - } - } -}

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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 -0.95,24269,14543,6828,3800,41079,47279,27177,17286,9802,7114,3756,85275,14507,34993,15139,15184,90742,27554,23713,6453,15157,7045,8048,47550,84540,93729,68601,6274,4713,30578,5024,94239,7315,8193,46871,96466,3695,70915,62947,32258,66228,2114,5084,12686,62905,19158,20940,36270,9037,34034,15016,15530,46276,11063,8586,15635,7196,70708,50836,22464,13463,86986,43541,2001,40565,28534,44700,5625,6552,16140,2450,8492,3304,22904,20951,100472,131147,131728,43674,514,79827,181148,31431,4761,1515,2075,138139,137795,71014,170145,60000,42790,179835,18982,48085,28398,56788,126115,5442,118289,9386 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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 diff --git a/convergenceAdvancedMCnegRew.txt b/convergenceAdvancedMCnegRew.txt index 9f6a895..228823b 100644 --- a/convergenceAdvancedMCnegRew.txt +++ b/convergenceAdvancedMCnegRew.txt @@ -17,3 +17,5 @@ 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zj<^ca0m5S;c%**TmoCinlO5sVg9Ci8#H9mk)o%j4(7g9 zd?}6_d@i?ICvlO1a4^ZQD9z%GuLh+06TBW70-5dWu3mt385y}hauRbL@j##5($v$g zdjzjlt-+`>rdofs{DFjaHXJ$(<>X&FOtLY8e-wa1+}yL{>YS>^rVMg+qk=$F(!fvh zWQ3qN9HfbWr;}>Tj>U02?nS`AKYlG)Ygs$Y4FV2WK_DkM6aoSPCq4h3udGX23$eSg S!M462;A5 Date: Thu, 2 Apr 2020 14:01:37 +0200 Subject: [PATCH 11/18] add constant for default reward --- .idea/misc.xml | 2 +- AdvancedZ3B1MonteCarlo.png | Bin 18582 -> 0 bytes AntAnalysis05Disc.png | Bin 28556 -> 0 bytes AntAnalysis099Disc.png | Bin 34712 -> 0 bytes AntAnalysis09Disc.png | Bin 27974 -> 0 bytes src/main/java/core/algo/Learning.java | 4 +--- .../algo/td/QLearningOffPolicyTDControl.java | 3 +-- src/main/java/evironment/antGame/AntWorld.java | 2 +- src/main/java/evironment/antGame/Reward.java | 1 + .../java/evironment/jumpingDino/DinoWorld.java | 4 ++-- src/main/java/example/ContinuousAnt.java | 4 +--- src/main/java/example/DinoSampling.java | 13 ++++++------- 12 files changed, 14 insertions(+), 19 deletions(-) delete mode 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zsh95Al1@S7{M%<{BB)fBcO|Y1ZI`IB(`vF{cdx6(#k2iAJ;Or<64R^zSUp|b*nRY| zmqQV$aIAN@vt9j==Xm(I80lE0I&uAeaPE4JR6hRM%X{R5k&rF1A%!)3C2{hZcVF2t z8Aqw&7ZYuA?SiXjhG2;@g#mlRfi9)p{O=#b;#Rkj bS-yR3Hps>4;Q;WQ7tiSvddDAYTK)N7<;0xc diff --git a/src/main/java/core/algo/Learning.java b/src/main/java/core/algo/Learning.java index 1bb5207..20a9ec1 100644 --- a/src/main/java/core/algo/Learning.java +++ b/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{ public void save(ObjectOutputStream oos) throws IOException { oos.writeObject(rewardHistory); - oos.writeObject(stateActionTable); + // oos.writeObject(stateActionTable); } public void load(ObjectInputStream ois) throws IOException, ClassNotFoundException { diff --git a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java index 89e0bc8..7375891 100644 --- a/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java +++ b/src/main/java/core/algo/td/QLearningOffPolicyTDControl.java @@ -7,7 +7,6 @@ import core.policy.GreedyPolicy; import core.policy.Policy; import evironment.antGame.Reward; import example.ContinuousAnt; -import example.DinoSampling; import java.io.File; import java.io.IOException; @@ -77,7 +76,7 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin foodCollected++; foodTimestampsTotal += timestampTilFood; if(foodCollected % 1000 == 0){ - System.out.println(foodTimestampsTotal/1000f); + System.out.println(foodTimestampsTotal / 1000f + " " + timestampCurrentEpisode); File file = new File(ContinuousAnt.FILE_NAME); try { Files.writeString(Path.of(file.getPath()), foodTimestampsTotal/1000f +",", StandardOpenOption.APPEND); diff --git a/src/main/java/evironment/antGame/AntWorld.java b/src/main/java/evironment/antGame/AntWorld.java index 9f35bfc..0a71a31 100644 --- a/src/main/java/evironment/antGame/AntWorld.java +++ b/src/main/java/evironment/antGame/AntWorld.java @@ -54,7 +54,7 @@ public class AntWorld implements Environment, 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()); diff --git a/src/main/java/evironment/antGame/Reward.java b/src/main/java/evironment/antGame/Reward.java index 3a910b8..c2e008d 100644 --- a/src/main/java/evironment/antGame/Reward.java +++ b/src/main/java/evironment/antGame/Reward.java @@ -1,6 +1,7 @@ package evironment.antGame; public class Reward { + public static final double DEFAULT_REWARD = -1; public static final double FOOD_PICK_UP_SUCCESS = 0; public static final double FOOD_PICK_UP_FAIL_NO_FOOD = -1; public static final double FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY = -1; diff --git a/src/main/java/evironment/jumpingDino/DinoWorld.java b/src/main/java/evironment/jumpingDino/DinoWorld.java index 06bb60f..5b60cd7 100644 --- a/src/main/java/evironment/jumpingDino/DinoWorld.java +++ b/src/main/java/evironment/jumpingDino/DinoWorld.java @@ -44,7 +44,7 @@ public class DinoWorld implements Environment, Visualizable { @Override public StepResultEnvironment step(DinoAction action) { boolean done = false; - int reward = 0; + int reward = 1; if(action == DinoAction.JUMP){ dino.jump(); @@ -68,7 +68,7 @@ public class DinoWorld implements Environment, Visualizable { spawnNewObstacle(); } if(ranIntoObstacle()) { - reward = -1; + reward = 0; done = true; } diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index 08308fa..e6d89dd 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -3,10 +3,8 @@ 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 evironment.antGame.AntWorldContinuousOriginalState; import java.io.File; import java.io.IOException; @@ -31,7 +29,7 @@ public class ContinuousAnt { rl.setNrOfEpisodes(1); //0.99 0.9 0.5 //0.99 0.95 0.9 0.7 0.5 0.3 0.1 - rl.setDiscountFactor(0.05f); + rl.setDiscountFactor(0.1f); // 0.1, 0.3, 0.5, 0.7 0.9 rl.setLearningRate(0.9f); rl.setEpsilon(0.2f); diff --git a/src/main/java/example/DinoSampling.java b/src/main/java/example/DinoSampling.java index 12fcdbc..c4bbde7 100644 --- a/src/main/java/example/DinoSampling.java +++ b/src/main/java/example/DinoSampling.java @@ -6,7 +6,6 @@ import core.controller.RLController; import core.controller.RLControllerGUI; import evironment.jumpingDino.DinoAction; import evironment.jumpingDino.DinoWorld; -import evironment.jumpingDino.DinoWorldAdvanced; import java.io.File; import java.io.IOException; @@ -34,13 +33,13 @@ public class DinoSampling { System.out.println("seed: " + i * 13); RNG.setSeed(i * 13); - RLController rl = new RLController<>( - new DinoWorldAdvanced(), - Method.Q_LEARNING_OFF_POLICY_CONTROL, + RLController rl = new RLControllerGUI<>( + new DinoWorld(), + Method.MC_CONTROL_FIRST_VISIT, DinoAction.values()); - rl.setDelay(0); - rl.setDiscountFactor(0.99f); - rl.setEpsilon(f); + rl.setDelay(300); + rl.setDiscountFactor(1f); + rl.setEpsilon(0.5f); rl.setLearningRate(0.9f); rl.setNrOfEpisodes(400000); rl.start(); From 9a3452ff9ceac26dc4fbfc7b2a95a4a4bd7a2e7c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Thu, 2 Apr 2020 15:56:11 +0200 Subject: [PATCH 12/18] add Every-Visit Monte-Carlo --- src/main/java/core/algo/EpisodicLearning.java | 2 +- src/main/java/core/algo/Method.java | 2 +- .../MonteCarloControlFirstVisitEGreedy.java | 90 +++++++++---------- .../java/core/controller/RLController.java | 6 +- 4 files changed, 52 insertions(+), 48 deletions(-) diff --git a/src/main/java/core/algo/EpisodicLearning.java b/src/main/java/core/algo/EpisodicLearning.java index d7b6655..6e57943 100644 --- a/src/main/java/core/algo/EpisodicLearning.java +++ b/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 extends Learning imple private void startLearning(){ dispatchLearningStart(); while(episodesToLearn.get() > 0){ + dispatchEpisodeStart(); timestampCurrentEpisode = 0; nextEpisode(); diff --git a/src/main/java/core/algo/Method.java b/src/main/java/core/algo/Method.java index 6372b24..99966fc 100644 --- a/src/main/java/core/algo/Method.java +++ b/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 } diff --git a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java b/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java index ff3247b..f00fa4b 100644 --- a/src/main/java/core/algo/mc/MonteCarloControlFirstVisitEGreedy.java +++ b/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.net.URI; -import java.nio.file.Files; -import java.nio.file.Path; -import java.nio.file.Paths; -import java.nio.file.StandardOpenOption; +import java.io.IOException; +import java.io.ObjectInputStream; +import java.io.ObjectOutputStream; import java.util.*; /** - * TODO: Major problem: - * StateActionPairs are only unique accounting for their position in the episode. - * For example: - *

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z8AHiPqW7w|+$ZiMPfnFvFW3OY_4hxTdcuj|$4g*Od2kOX)3-VoKjil1txVo7&mC^{ zG}PKRBvv*HyIm2BdGq?|&*kY)GwYN6Evl*(Z>ur2J~LkJC?vFaGT1ntkMyE4pu+Fh z?BoZrW>HF6LQ{B3sfw>j6<$>}4m&xw2Uk2ZhLpK@r(P|V{Hwq8AnGh55{UKMcSfJz zAbi>+(g^%o6dxxpiPOmr1BwB?kDDHBC?2V*YI~DzKI5>XJ<1HnITK;5gnXe6*u1_L zdBAsagwj@cVSv=5-7W3!tvWGmJiw>c;^OsCS+&1lqQKuh%TK4gf0jPy{u1I@hx^9w zjDXY?8_ci+J&rs=IyqR>jWMH8T;+|~+A~9A)bbUam?>rczi$4iHGZ@3eYk6RHg5xb zuVUl(yAKARvJN{9w7l@HxKrJWws7HF@0; extends EpisodicLearnin int timestampTilFood = 0; int rewardsPer1000 = 0; int foodCollected = 0; + int iterations = 0; + int foodTimestampsTotal= 0; while(envResult == null || !envResult.isDone()) { actionValues = stateActionTable.getActionValues(state); A action = policy.chooseAction(actionValues); @@ -57,11 +59,10 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin double reward = envResult.getReward(); State nextState = envResult.getState(); sumOfRewards += reward; - rewardsPer1000+=reward; timestampTilFood++; - if(foodCollected == 10000){ + /* if(iterations == 100){ File file = new File(ContinuousAnt.FILE_NAME); try { Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); @@ -69,17 +70,46 @@ public class QLearningOffPolicyTDControl extends EpisodicLearnin e.printStackTrace(); } return; - } + }*/ + + if(reward == Reward.FOOD_DROP_DOWN_SUCCESS){ foodCollected++; - File file = new File(ContinuousAnt.FILE_NAME); - try { - Files.writeString(Path.of(file.getPath()), timestampTilFood + ",", StandardOpenOption.APPEND); - } catch (IOException e) { - e.printStackTrace(); + foodTimestampsTotal += timestampTilFood; + if(foodCollected % 1000 == 0){ + System.out.println(foodTimestampsTotal/1000f); + File file = new File(ContinuousAnt.FILE_NAME); + try { + Files.writeString(Path.of(file.getPath()), foodTimestampsTotal/1000f +",", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } + foodTimestampsTotal = 0; } + if(foodCollected == 1000){ + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.15f); + } + if(foodCollected == 2000){ + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.10f); + } + if(foodCollected == 3000){ + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.05f); + } + if(foodCollected == 4000){ + System.out.println("final 0 expl"); + ((EpsilonGreedyPolicy) this.policy).setEpsilon(0.00f); + } + if(foodCollected == 15000){ + File file = new File(ContinuousAnt.FILE_NAME); + try { + Files.writeString(Path.of(file.getPath()), "\n", StandardOpenOption.APPEND); + } catch (IOException e) { + e.printStackTrace(); + } + return; + } + iterations++; timestampTilFood = 0; - rewardsPer1000 = 0; } // Q Update diff --git a/src/main/java/evironment/antGame/Reward.java b/src/main/java/evironment/antGame/Reward.java index 05e7a1f..3a910b8 100644 --- a/src/main/java/evironment/antGame/Reward.java +++ b/src/main/java/evironment/antGame/Reward.java @@ -1,13 +1,13 @@ package evironment.antGame; public class Reward { - public static final double FOOD_PICK_UP_SUCCESS = 1; + public static final double FOOD_PICK_UP_SUCCESS = 0; public static final double FOOD_PICK_UP_FAIL_NO_FOOD = -1; public static final double FOOD_PICK_UP_FAIL_HAS_FOOD_ALREADY = -1; public static final double FOOD_DROP_DOWN_FAIL_NO_FOOD = -1; public static final double FOOD_DROP_DOWN_FAIL_NOT_START = -1; - 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; diff --git a/src/main/java/example/ContinuousAnt.java b/src/main/java/example/ContinuousAnt.java index 9005956..08308fa 100644 --- a/src/main/java/example/ContinuousAnt.java +++ b/src/main/java/example/ContinuousAnt.java @@ -12,8 +12,10 @@ import java.io.File; import java.io.IOException; public class ContinuousAnt { - public static final String FILE_NAME = "converge05.txt"; + public static final String FILE_NAME = "converge22.txt"; public static void main(String[] args) { + int i = 4+4+4+6+6+6+8+10+12+14+14+16+16+16+18+18+18+20+20+20+22+22+22+24+24+24+24+26+26+26+26+26+28+28+28+28+28+30+30+30+30+32+32+32+34+34+34+36+36+38+40+42; + System.out.println(i/52f); File file = new File(FILE_NAME); try { file.createNewFile(); @@ -27,9 +29,12 @@ public class ContinuousAnt { AntAction.values()); rl.setDelay(0); rl.setNrOfEpisodes(1); - rl.setDiscountFactor(0.7f); - rl.setLearningRate(0.2f); - rl.setEpsilon(0.5f); + //0.99 0.9 0.5 + //0.99 0.95 0.9 0.7 0.5 0.3 0.1 + rl.setDiscountFactor(0.05f); + // 0.1, 0.3, 0.5, 0.7 0.9 + rl.setLearningRate(0.9f); + rl.setEpsilon(0.2f); rl.start(); } } diff --git a/src/main/java/example/JumpingDino.java b/src/main/java/example/JumpingDino.java index b738fbb..0a03b16 100644 --- a/src/main/java/example/JumpingDino.java +++ b/src/main/java/example/JumpingDino.java @@ -17,9 +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(); } From f4b50627d19c1d523276472282291416dd3261e9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jan=20L=C3=B6wenstrom?= Date: Sun, 29 Mar 2020 17:22:01 +0200 Subject: [PATCH 09/18] add antGame analysis data and R Scripts and images --- AdvancedZ3B1MonteCarlo.png | Bin 0 -> 18582 bytes AntAnalysis05Disc.png | Bin 0 -> 28556 bytes AntAnalysis099Disc.png | Bin 0 -> 34712 bytes AntAnalysis09Disc.png | Bin 0 -> 27974 bytes DiscAndLRonQLearningAdvanced.R | 56 ++++++++++++++++++ EpsilonAnalysis.R | 23 +++++++ NeededTimestampsFromEverywhere.png | Bin 0 -> 13379 bytes OptimalityAdancedDiffDiscounts.txt | 9 +++ OptimalityDifferentDiscountFactors.R | 37 ++++++++++++ SimpleZ1B2QLearning.png | Bin 0 -> 18871 bytes SimpleZ1B2SARSA.png | Bin 0 -> 18563 bytes SimpleZ2B1MonteCarlo.png | Bin 0 -> 18298 bytes SimpleZ2B1QLearning.png | Bin 0 -> 18791 bytes SimpleZ2B1QLearningMaxValueAsDefault.png | Bin 0 -> 20347 bytes SimpleZ2B2QLearning.png | Bin 0 -> 18467 bytes SimpleZ2B2SARSA.png | Bin 0 -> 18163 bytes SimpleZ2B4QLearning.png | Bin 0 -> 17258 bytes antAnalysisAvgReward.txt | 15 +++++ convResAdvScale.png | Bin 8067 -> 0 bytes convResultAdv.png | Bin 8856 -> 0 bytes convResultSimple.png | Bin 7659 -> 0 bytes convergencSimpleQLearning099disc09LR.txt | 21 +++++++ convergenceAdvanced.txt | 2 +- convergenceAdvancedMCnegRew.txt | 19 ++++++ convergenceSimple.txt | 21 +++++++ ...SimpleNegRewInJumpQLearning099disc09LR.txt | 21 +++++++ convergenceSimpleNoJumpInfo.txt | 21 +++++++ ...nceSimpleNoJumpingQLearning099disc09LR.txt | 21 +++++++ ...ergenceSimpleNoJumpingSARSA099disc09LR.txt | 21 +++++++ ...genceSimpleQ099Disc09LRSameRewardsAsMC.txt | 21 +++++++ ...c09LRSameRewardsAsMCMinus1DefaultValue.txt | 21 +++++++ convergenceSimpleSARSA099disc09LR.txt | 21 +++++++ epsilonValues.R | 51 +++++++++++++--- optimalityDiffDiscount.png | Bin 0 -> 18230 bytes .../algo/td/QLearningOffPolicyTDControl.java | 48 ++++++++++++--- src/main/java/evironment/antGame/Reward.java | 4 +- src/main/java/example/ContinuousAnt.java | 13 ++-- src/main/java/example/JumpingDino.java | 4 +- 38 files changed, 445 insertions(+), 25 deletions(-) create mode 100644 AdvancedZ3B1MonteCarlo.png create mode 100644 AntAnalysis05Disc.png create mode 100644 AntAnalysis099Disc.png create mode 100644 AntAnalysis09Disc.png create mode 100644 DiscAndLRonQLearningAdvanced.R create mode 100644 EpsilonAnalysis.R create mode 100644 NeededTimestampsFromEverywhere.png create mode 100644 OptimalityAdancedDiffDiscounts.txt create mode 100644 OptimalityDifferentDiscountFactors.R create mode 100644 SimpleZ1B2QLearning.png create mode 100644 SimpleZ1B2SARSA.png create mode 100644 SimpleZ2B1MonteCarlo.png create mode 100644 SimpleZ2B1QLearning.png create mode 100644 SimpleZ2B1QLearningMaxValueAsDefault.png create mode 100644 SimpleZ2B2QLearning.png create mode 100644 SimpleZ2B2SARSA.png create mode 100644 SimpleZ2B4QLearning.png create mode 100644 antAnalysisAvgReward.txt delete mode 100644 convResAdvScale.png delete mode 100644 convResultAdv.png delete mode 100644 convResultSimple.png create mode 100644 convergencSimpleQLearning099disc09LR.txt create mode 100644 convergenceAdvancedMCnegRew.txt create mode 100644 convergenceSimple.txt create mode 100644 convergenceSimpleNegRewInJumpQLearning099disc09LR.txt create mode 100644 convergenceSimpleNoJumpInfo.txt create mode 100644 convergenceSimpleNoJumpingQLearning099disc09LR.txt create mode 100644 convergenceSimpleNoJumpingSARSA099disc09LR.txt create mode 100644 convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt create mode 100644 convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt create mode 100644 convergenceSimpleSARSA099disc09LR.txt create mode 100644 optimalityDiffDiscount.png diff --git a/AdvancedZ3B1MonteCarlo.png b/AdvancedZ3B1MonteCarlo.png new file mode 100644 index 0000000000000000000000000000000000000000..6d2eb8b76832f75743d40ffd470d176069408650 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bS-yR3Hps>4;Q;WQ7tiSvddDAYTK)N7<;0xc literal 0 HcmV?d00001 diff --git a/DiscAndLRonQLearningAdvanced.R b/DiscAndLRonQLearningAdvanced.R new file mode 100644 index 0000000..6df2cd4 --- /dev/null +++ b/DiscAndLRonQLearningAdvanced.R @@ -0,0 +1,56 @@ +# Libraries +library(ggplot2) +library(matrixStats) +ta <- as.matrix(read.table(file.choose(), sep=",", header = FALSE)) +ta <- t(ta) +dim(ta) +head(ta) + +# Create dummy data +data <- data.frame( + y=ta[,1], + y2=ta[,2], + y3=ta[,3], + y4=ta[,4], + y5=ta[,5], + y6=ta[,6], + y7=ta[,7], + y8=ta[,8], + y9=ta[,9], + y10=ta[,10], + y11=ta[,11], + y12=ta[,12], + y13=ta[,13], + y14=ta[,14], + y15=ta[,15], + x=seq(1, length(ta[,1])) +) +ggplot(data, aes(x*1000)) + + labs(title="Diskontierungsfaktor = 0.99", + x ="Zeitstempel", y = "Ø Belohnung pro Zeitstempel", color = "Lernrate") + + ylim(-1.5,0.6) + + geom_line(aes(y = y, colour = "0.1"), size=1)+ + geom_line(aes(y = y2, colour = "0.3"), size=1) + + geom_line(aes(y = y3, colour = "0.5"), size=1) + + geom_line(aes(y = y4, colour = "0.7"), size=1) + + geom_line(aes(y = y5, colour = "0.9"), size=1) + +ggplot(data, aes(x*1000)) + + labs(title="Diskontierungsfaktor = 0.9", + x ="Zeitstempel", y = "Ø Belohnung pro Zeitstempel", color = "Lernrate") + + ylim(-1.5,0.6) + + geom_line(aes(y = y6, colour = "0.1"), size=1) + + geom_line(aes(y = y7, colour = "0.3"), size=1) + + geom_line(aes(y = y8, colour = "0.5"), size=1) + + geom_line(aes(y = y9, colour = "0.7"), size=1) + + geom_line(aes(y = y10, colour = "0.9"), size=1) + +ggplot(data, aes(x*1000) ) + + labs(title="Diskontierungsfaktor = 0.5", + x ="Zeitstempel", y = "Ø Belohnung pro Zeitstempel", color = "Lernrate") + + ylim(-1.5,0.6) + + geom_line(aes(y = y11, colour = "0.1"), size=1) + + geom_line(aes(y = y12, colour = "0.3"), size=1) + + geom_line(aes(y = y13, colour = "0.5"), size=1) + + geom_line(aes(y = y14, colour = "0.7"), size=1) + + geom_line(aes(y = y15, colour = "0.9"), size=1) \ No newline at end of file diff --git a/EpsilonAnalysis.R b/EpsilonAnalysis.R new file mode 100644 index 0000000..3190686 --- /dev/null +++ b/EpsilonAnalysis.R @@ -0,0 +1,23 @@ +# Libraries +library(ggplot2) +library(matrixStats) +convergence <- read.csv(file.choose(), header=FALSE, row.names=1, skip=1) + +sds <- rowSds(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) +men <- rowMeans(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE) +print(sds) + +# create dummy data +data <- data.frame( + names=rownames(convergence), + means=men, + sds=sds +) + +convergence$group <- row.names(convergence) +convergence.m <- melt(convergence, id.vars = "groups") + +ggplot(data) + + geom_bar(aes(x=names, y=means, fill=means), stat="identity", colour="black", alpha=0.8) + + geom_errorbar( aes(x=names, ymin=means, ymax=means+sds), width=0.4, colour="black", alpha=0.8, size=0.6) + + xlab("Epsilon") + ylab("Ø Episoden bis Konvergenz") \ No newline at end of file diff --git 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