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Commit 8125bfb7 authored by BrunoDatoMeneses's avatar BrunoDatoMeneses
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ADD: reinforcement multiUI

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AMOEBAonAMAK/src/experiments/reinforcement/SimpleReinforcement1DSpatialRewardAndActionMltiUI.java 0 → 100644
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package experiments.reinforcement;
import java.io.File;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Random;
import agents.context.localModel.TypeLocalModel;
import agents.percept.Percept;
import experiments.nDimensionsLaunchers.F_N_Manager;
import fr.irit.smac.amak.Configuration;
import fr.irit.smac.amak.tools.Log;
import fr.irit.smac.amak.ui.VUIMulti;
import fr.irit.smac.amak.ui.drawables.Drawable;
import gui.AmoebaMultiUIWindow;
import gui.AmoebaWindow;
import javafx.application.Application;
import javafx.application.Platform;
import javafx.beans.value.ChangeListener;
import javafx.beans.value.ObservableValue;
import javafx.scene.control.Slider;
import javafx.stage.Stage;
import kernel.AMOEBA;
import kernel.StudiedSystem;
import kernel.World;
import kernel.backup.BackupSystem;
import kernel.backup.IBackupSystem;
import kernel.backup.SaveHelperDummy;
import kernel.backup.SaveHelperImpl;
import utils.Pair;
import utils.RandomUtils;
import utils.TRACE_LEVEL;
import utils.XmlConfigGenerator;
/**
* Train an amoeba on a simple reinforcement task.
* The goal of the task is to get to the center. When the position of the agent cross 0, it gets a reward of 100.
* The agent can only moves in 2 directions, of a distance of 1. Moving give a reward of -1.
* If the agent moves outside of the allowed range, it gets a reward of -100.
* @author Hugo
*
*/
public abstract class SimpleReinforcement1DSpatialRewardAndActionMltiUI extends Application implements Serializable{
AMOEBA amoebaSpatialReward;
VUIMulti amoebaSpatialRewardVUI;
AmoebaMultiUIWindow amoebaSpatialRewardUI;
AMOEBA amoebaControlModel;
VUIMulti amoebaControlModelVUI;
AmoebaMultiUIWindow amoebaControlModelUI;
/* Learn and Test */
public static final int MAX_STEP_PER_EPISODE = 200;
public static final int N_LEARN = 1000;//400
public static final int N_TEST = 100;
/* Exploration */
public static final double MIN_EXPLO_RATE = 0.02;
public static final double EXPLO_RATE_DIMINUTION_FACTOR = 0.01;
public static final double EXPLO_RATE_BASE = 1;
public static void main(String[] args) throws IOException {
Application.launch(args);
}
@Override
public void start(Stage arg0) throws Exception, IOException {
Configuration.multiUI=true;
Configuration.commandLineMode = false;
Configuration.allowedSimultaneousAgentsExecution = 1;
Configuration.waitForGUI = true;
Configuration.plotMilliSecondsUpdate = 20000;
amoebaSpatialRewardVUI = new VUIMulti("2D");
amoebaSpatialRewardUI = new AmoebaMultiUIWindow("SPATIAL_REWARD", amoebaSpatialRewardVUI);
amoebaControlModelVUI = new VUIMulti("2D");
amoebaControlModelUI = new AmoebaMultiUIWindow("CONTROL_MODEL", amoebaControlModelVUI);
//startTask(100, 1000);
}
public void startTask(long wait, int cycles)
{
// Create a Runnable
Runnable task = new Runnable()
{
public void run()
{
runTask(wait, cycles);
}
};
// Run the task in a background thread
Thread backgroundThread = new Thread(task);
// Terminate the running thread if the application exits
backgroundThread.setDaemon(true);
// Start the thread
backgroundThread.start();
}
public void startTask2(long wait, int cycles)
{
// Create a Runnable
Runnable task = new Runnable()
{
public void run()
{
runTask2(wait, cycles);
}
};
// Run the task in a background thread
Thread backgroundThread = new Thread(task);
// Terminate the running thread if the application exits
backgroundThread.setDaemon(true);
// Start the thread
backgroundThread.start();
}
public void runTask(long wait, int cycles)
{
try
{
// Update the Label on the JavaFx Application Thread
Platform.runLater(new Runnable()
{
@Override
public void run()
{
ArrayList<ArrayList<Double>> results = new ArrayList<>();
//LearningAgent agent = new QLearning();
LearningAgent agent = new AmoebaQL();
//LearningAgent agent = new AmoebaCoop();
Environment env = new OneDimensionEnv(10);
results.add(learning(agent, env));
int nbEpisodes = results.get(0).size();
for(int i = 0; i < nbEpisodes; i++) {
double average = 0;
for(int j = 0; j < results.size(); j++) {
average += results.get(j).get(i);
}
average /= results.size();
System.out.println(""+i+"\t"+average);
}
}
});
Thread.sleep(wait);
}
catch (InterruptedException e)
{
e.printStackTrace();
}
// for(int i = 0; i < cycles; i++)
// {
// try
// {
// // Get the Status
// final String status = "Processing " + i + " of " + cycles;
//
// // Update the Label on the JavaFx Application Thread
// Platform.runLater(new Runnable()
// {
// @Override
// public void run()
// {
// ///
// }
// });
//
// Thread.sleep(wait);
// }
// catch (InterruptedException e)
// {
// e.printStackTrace();
// }
// }
}
public void runTask2(long wait, int cycles)
{
try
{
// Update the Label on the JavaFx Application Thread
Platform.runLater(new Runnable()
{
@Override
public void run()
{
///
}
});
Thread.sleep(wait);
}
catch (InterruptedException e)
{
e.printStackTrace();
}
for(int i = 0; i < cycles; i++)
{
try
{
// Get the Status
final String status = "Processing " + i + " of " + cycles;
// Update the Label on the JavaFx Application Thread
Platform.runLater(new Runnable()
{
@Override
public void run()
{
///
}
});
Thread.sleep(wait);
}
catch (InterruptedException e)
{
e.printStackTrace();
}
}
}
@Override
public void stop() throws Exception {
super.stop();
System.exit(0);
}
/**
* An environment in which a LearningAgent reside
* @author Hugo
*
*/
public interface Environment {
public List<String> actionSpace();
public List<String> perceptionSpace();
public HashMap<String, Double> reset();
public HashMap<String, Double> step(HashMap<String, Double> action);
public HashMap<String, Double> randomAction();
}
/**
* Wrapper for any kind of learning agent
* @author Hugo
*
*/
public interface LearningAgent {
public HashMap<String, Double> choose(HashMap<String, Double> state, Environment env);
public HashMap<String, Double> explore(HashMap<String, Double> state, Environment env);
public void learn(HashMap<String, Double> state, HashMap<String, Double> state2, HashMap<String, Double> action, boolean done);
}
/**
* Compatible only with OneDimensionEnv
* @author Hugo
*
*/
public static class AmoebaQL implements LearningAgent {
public AMOEBA amoebaSpatialReward;
//public AMOEBA amoebaControlModel;
public double lr = 0.8;
public double gamma = 0.9;
private Random rand = new Random();
public AmoebaQL() {
amoebaSpatialReward = setupSpatialReward();
//amoebaControlModel = setupControlModel();
}
@Override
public HashMap<String, Double> choose(HashMap<String, Double> state, Environment env) {
// HashMap<String, Double> stateWithVizuAdded = new HashMap<String, Double>(state);
// stateWithVizuAdded.put("p2", 0.0);
// stateWithVizuAdded.put("oracle", 0.0);
// HashMap<String, Double> bestFuturePosition = amoebaSpatialReward.reinforcementRequest(stateWithVizuAdded);
//
// HashMap<String, Double> action = new HashMap<String, Double>();
// if(bestFuturePosition!=null) {
// HashMap<String, Double> requestForControlModel = new HashMap<String, Double>();
// requestForControlModel.put("pCurrent", state.get("p1"));
// requestForControlModel.put("pGoal", bestFuturePosition.get("p1"));
//
// double bestAction = amoebaControlModel.request(requestForControlModel);
//
//
// action.put("a1", bestAction);
// }
// action = env.randomAction();
//
// return action;
return null;
}
@Override
public void learn(HashMap<String, Double> state, HashMap<String, Double> positionAndReward,
HashMap<String, Double> action, boolean done) {
// state : previous position and associated reward
// state2 : new position with current reward
// action : previous state, current actions and current reward
HashMap<String, Double> previousStateCurrentStateAction = new HashMap<>();
previousStateCurrentStateAction.put("pCurrent", action.get("p1"));
previousStateCurrentStateAction.put("pGoal", positionAndReward.get("p1"));
previousStateCurrentStateAction.put("oracle", action.get("a1"));
//System.out.println("ControlModel " + previousStateCurrentStateAction + " ---------------- SIMPLE REIN XP 149");
//System.out.println("SpatialReward " + positionAndReward + " ---------------- SIMPLE REIN XP 149");
amoebaSpatialReward.learn(positionAndReward);
//amoebaControlModel.learn(previousStateCurrentStateAction);
}
@Override
public HashMap<String, Double> explore(HashMap<String, Double> state, Environment env) {
return env.randomAction();
}
}
/**
* Compatible only with OneDimensionEnv.<br/>
* An extremely crude and quick implementation of Q learning.
* Not expected to perform well, but should be better than random.
* @author Hugo
*
*/
public static class QLearning implements LearningAgent {
public double[][] Q = new double[102][2];
public double lr = 0.8;
public double gamma = 0.9;
private Random rand = new Random();
@Override
public HashMap<String, Double> choose(HashMap<String, Double> state, Environment env) {
int p = state.get("p1").intValue()+50;
double a;
if(Q[p][0] == Q[p][1]) {
a = rand.nextBoolean() ? -1 : 1;
} else {
a = Q[p][0] > Q[p][1] ? -1 : 1;
}
HashMap<String, Double> action = new HashMap<String, Double>();
action.put("a1", a);
return action;
}
@Override
public void learn(HashMap<String, Double> state, HashMap<String, Double> state2,
HashMap<String, Double> action, boolean done) {
int p = state.get("p1").intValue()+50;
int p2 = state2.get("p1").intValue()+50;
int a = action.get("a1").intValue() == -1 ? 0 : 1;
double reward = state2.get("oracle");
double max = Double.NEGATIVE_INFINITY;
if(!done) {
for(Double v : Q[p2]) {
max = Math.max(max, v);
}
} else {
max = reward;
}
//
double q = reward + gamma * max - Q[p][a];
Q[p][a] += lr * q;
}
@Override
public HashMap<String, Double> explore(HashMap<String, Double> state, Environment env) {
return env.randomAction();
}
}
public static class OneDimensionEnv implements Environment {
private Random rand = new Random();
private double x = 0;
private double reward = 0;
private double size;
private Drawable pos;
public OneDimensionEnv(double envSize) {
size = envSize;
if(!Configuration.commandLineMode) {
AmoebaWindow instance = AmoebaWindow.instance();
//pos = new DrawableOval(0.5, 0.5, 1, 1);
//pos.setColor(new Color(0.5, 0.0, 0.0, 0.5));
//instance.mainVUI.add(pos);
//instance.mainVUI.createAndAddRectangle(-50, -0.25, 100, 0.5);
//instance.mainVUI.createAndAddRectangle(-0.25, -1, 0.5, 2);
instance.point.hide();
//instance.rectangle.hide();
}
}
@Override
public HashMap<String, Double> reset(){
x = RandomUtils.nextDouble(rand, -size, Math.nextUp(size));
x = Math.round(x);
reward = 0.0;
//pos.move(x+0.5, 0.5);
HashMap<String, Double> ret = new HashMap<>();
ret.put("p1", x);
ret.put("oracle", reward);
return ret;
}
@Override
public HashMap<String, Double> step(HashMap<String, Double> actionMap){
double action = actionMap.get("a1");
//if(action == 0.0) action = rand.nextDouble();
if(action > 0.0) action = Math.ceil(action);
if(action < 0.0 ) action = Math.floor(action);
if(action > 1.0) action = 1.0;
if(action < -1.0) action = -1.0;
double oldX = x;
x = x + action;
//System.out.println("ACTIONS " + " a1 " +action + " " + " a2 " + action2);
if(x < -size || x > size) {
reward = -1000.0;
} else if((x == 0.0) || (sign(oldX) != sign(x) )) {
// win !
reward = 1000.0;
} else {
reward = -1.0;
}
HashMap<String, Double> ret = new HashMap<>();
ret.put("p1", x);
ret.put("oracle", reward);
//pos.move(x+0.5, 0.5);
return ret;
}
@Override
public List<String> actionSpace() {
ArrayList<String> l = new ArrayList<>();
l.add("a1 enum:true {-1, 0, 1}");
return l;
}
@Override
public List<String> perceptionSpace() {
ArrayList<String> l = new ArrayList<>();
l.add("p1 enum:false [-"+size+", "+size+"]");
return l;
}
@Override
public HashMap<String, Double> randomAction() {
double a1 = rand.nextBoolean() ? -1 : 1;
HashMap<String, Double> action = new HashMap<String, Double>();
action.put("a1", a1);
return action;
}
}
/**
* Setup an amoeba for the SimpleReinforcement problem
* @return
*/
private static AMOEBA setup() {
ArrayList<Pair<String, Boolean>> sensors = new ArrayList<>();
sensors.add(new Pair<String, Boolean>("p1", false));
File config;
try {
config = File.createTempFile("config", "xml");
XmlConfigGenerator.makeXML(config, sensors);
} catch (IOException e) {
e.printStackTrace();
System.exit(1);
return null; // now compilator know config is initialized
}
//File config = new File("resources/simpleReinManualTrained.xml");
Log.defaultMinLevel = Log.Level.INFORM;
World.minLevel = TRACE_LEVEL.ERROR;
AMOEBA amoeba = new AMOEBA(null, null, config.getAbsolutePath(), null);
amoeba.saver = new SaveHelperDummy();
return amoeba;
}
private static AMOEBA setupSpatialReward() {
ArrayList<Pair<String, Boolean>> sensors = new ArrayList<>();
sensors.add(new Pair<String, Boolean>("p1", false));
File config;
try {
config = File.createTempFile("configSpatialReward", "xml");
XmlConfigGenerator.makeXML(config, sensors);
} catch (IOException e) {
e.printStackTrace();
System.exit(1);
return null; // now compilator know config is initialized
}
//File config = new File("resources/simpleReinManualTrained.xml");
Log.defaultMinLevel = Log.Level.INFORM;
World.minLevel = TRACE_LEVEL.ERROR;
AMOEBA amoeba = new AMOEBA(null, null, config.getAbsolutePath(), null);
amoeba.saver = new SaveHelperDummy();
amoeba.setLocalModel(TypeLocalModel.MILLER_REGRESSION);
amoeba.getEnvironment().setMappingErrorAllowed(0.025);
//amoeba.setReinforcement(true);
return amoeba;
}
private static AMOEBA setupControlModel() {
ArrayList<Pair<String, Boolean>> sensors = new ArrayList<>();
sensors.add(new Pair<String, Boolean>("pCurrent", false));
sensors.add(new Pair<String, Boolean>("pGoal", false));
File config;
try {
config = File.createTempFile("configControlModel", "xml");
XmlConfigGenerator.makeXML(config, sensors);
} catch (IOException e) {
e.printStackTrace();
System.exit(1);
return null; // now compilator know config is initialized
}
//File config = new File("resources/simpleReinManualTrained.xml");
Log.defaultMinLevel = Log.Level.INFORM;
World.minLevel = TRACE_LEVEL.ERROR;
AMOEBA amoeba = new AMOEBA(null, null, config.getAbsolutePath(), null);
amoeba.saver = new SaveHelperDummy();
amoeba.setLocalModel(TypeLocalModel.MILLER_REGRESSION);
amoeba.getEnvironment().setMappingErrorAllowed(0.025);
return amoeba;
}
/**
* Teach a learning agent on the SimpleReinforcement problem
* @param agent
* @return
*/
public static ArrayList<Double> learning(LearningAgent agent, Environment env){
ArrayList<Double> averageRewards = new ArrayList<Double>();
Random rand = new Random();
Random r = new Random();
HashMap<String, Double> state = env.reset();
HashMap<String, Double> state2;
double explo = EXPLO_RATE_BASE;
for(int i = 0; i < N_LEARN; i++) {
int nbStep = 0;
state = env.reset();
HashMap<String, Double> action = new HashMap<String, Double>();
double totReward = 0.0;
// execute simulation cycles
boolean done = false;
boolean invalid = false;
while(!done && !invalid) {
nbStep++;
if(nbStep > MAX_STEP_PER_EPISODE) {
invalid = true;
}
state.remove("oracle");
action = new HashMap<String, Double>();
action = agent.explore(state, env);
// if(rand.nextDouble() < explo) {
// action = agent.explore(state, env);
// } else {
// action = agent.choose(state, env);
// }
state2 = env.step(action); // new position with associated reward
if(state2.get("oracle") != -1.0) { //if goal or end of world
done = true;
}
action.put("p1", state.get("p1")); //add previous state to action
action.put("oracle", state2.get("oracle")); //add current reward to action
// state : previous position and associated reward
// state2 : new position with current reward
// action : previous state, current action and current reward
agent.learn(state, state2, action, done);
totReward += action.get("oracle");
state = state2;
}
System.out.println("-----------------------------------------------------------------------");
// update exploration rate
if(explo > MIN_EXPLO_RATE) {
explo -= EXPLO_RATE_DIMINUTION_FACTOR;
if(explo < MIN_EXPLO_RATE)
explo = MIN_EXPLO_RATE;
}
System.out.println("Episode "+i+" reward : "+totReward+" explo : "+explo);
//double testAR = test(agent, env, r, N_TEST);
//averageRewards.add(testAR);
//Scanner scan = new Scanner(System.in);
//scan.nextLine();
}
return averageRewards;
}
private static double test(LearningAgent agent, Environment env, Random r, int nbTest) {
HashMap<String, Double> state;
HashMap<String, Double> state2;
double nbPositiveReward = 0.0;
double tot_reward = 0.0;
for(int i = 0; i < nbTest; i++) {
double reward = 0.0;
state = env.reset();
// execute simulation cycles
boolean done = false;
int nbStep = 0;
while(!done) {
nbStep++;
if(nbStep > 200) {
done = true;
}
state.remove("oracle");
HashMap<String, Double> a = agent.choose(state, env);
state2 = env.step(a);
if(state2.get("oracle") != -1.0) {
done = true;
}
reward += state2.get("oracle");
state = state2;
}
if(reward > 0) {
nbPositiveReward += 1.0;
}
tot_reward += reward;
}
double averageReward = tot_reward/nbTest;
System.out.println("Test average reward : "+averageReward+" Positive reward %: "+(nbPositiveReward/nbTest));
return averageReward;
}
/**
* This is a proof of concept, showing that if amoeba learn the correct model of the reward,
* it can produce a good solution.
* The expected average reward for the optimal solution is 75.
* The main cause of negative reward is infinite loop (usually near the objective). In such case, the reward is -200
*/
public static void poc(boolean learnMalus) {
AMOEBA amoeba = setup();
Environment env = new OneDimensionEnv(50);
// train
for(double n = 0.0; n < 0.5; n+=0.1) {
double pos = 50.0-n;
for(int i = 0; i < 49; i++) {
double reward = 100 - Math.abs(pos);
HashMap<String, Double> action = new HashMap<String, Double>();
action.put("p1", pos);
action.put("a1", -1.0);
action.put("oracle", reward);
amoeba.learn(action);
if(learnMalus) {
reward = -150 + Math.abs(pos);
action.put("a1", 1.0);
action.put("oracle", reward);
amoeba.learn(action);
}
pos -= 1.0;
}
pos = -50.0-n;
for(int i = 0; i < 49; i++) {
double reward = 100 - Math.abs(pos);
HashMap<String, Double> action = new HashMap<String, Double>();
action.put("p1", pos);
action.put("a1", 1.0);
action.put("oracle", reward);
amoeba.learn(action);
if(learnMalus) {
reward = -150 + Math.abs(pos);
action.put("a1", -1.0);
action.put("oracle", reward);
amoeba.learn(action);
}
pos += 1.0;
}
}
// tests
Random r = new Random();
HashMap<String, Double> state = env.reset();
HashMap<String, Double> state2;
double tot_reward = 0.0;
int nbTest = 100;
double nbPositiveReward = 0;
for(int i = 0; i < nbTest; i++) {
double reward = 0.0;
state = env.reset();
HashMap<String, Double> action = new HashMap<String, Double>();
// execute simulation cycles
boolean done = false;
int nbStep = 0;
while(!done) {
nbStep++;
if(nbStep > 200) {
done = true;
}
state.remove("oracle");
action = amoeba.maximize(state);
// random action if no proposition from amoeba
if(action.get("oracle").equals(Double.NEGATIVE_INFINITY) ) {
action.put("a1", (r.nextBoolean() ? 1.0 : -1.0));
}
//System.out.println("action "+action);
state2 = env.step(action);
if(state2.get("oracle") != -1.0) {
done = true;
}
reward += state2.get("oracle");
//System.out.println("state2 "+state2+" reward "+reward);
state = state2;
}
if(reward > 0) {
nbPositiveReward += 1.0;
}
tot_reward += reward;
//System.out.println("-----------------------------\nTot reward "+tot_reward+"\n-----------------------------");
}
System.out.println("Average reward : "+tot_reward/nbTest+" Positive reward %: "+(nbPositiveReward/nbTest));
}
private static int sign(double x) {
return x < 0 ? -1 : 1;
}
}
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