public Experiment(SimulatorExperiment exp)
{
simExp = exp;
inputs = exp.getNumCPPNInputs();
outputs = exp.getNumCPPNOutputs();
outputsPerPolicy = exp.getNumCPPNOutputsPerModule();
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
double fitness = 0.0f;
objectives = null;
if (environment.name.Equals("ENV_dual_task.xml")) //HACK navigation
{
fitness = (1.0f - ip.robots[0].location.distance(new Point2D(environment.POIPosition[4].X, environment.POIPosition[4].Y)) / 650.0f);
if (fitness < 0) fitness = 0.00001f;
if (reachedGoal) fitness = 1;
// fitness = 1;
}
else //food gathering
{
float distFood = (float)(1.0f - (engine.robots[0].location.distance(environment.goal_point) / environment.maxDistance));
fitness = (collectedFood + distFood) / 4.0f;
if (fitness < 0) fitness = 0.00001f;
if (collectedFood >= 4) fitness = 1.0f;
// fitness = 1;
}
return fitness;
}
public Experiment(SimulatorExperiment exp)
{
simExp = exp;
inputs = exp.getNumCPPNInputs();
outputs = exp.getNumCPPNOutputs();
outputsPerPolicy = exp.getNumCPPNOutputsPerModule();
}
//characterizing behavior...
List<double> IBehaviorCharacterization.calculate(SimulatorExperiment exp)
{
bool disabled=false;
for(int i=0;i<exp.robots.Count;i++) {
if(exp.robots[i].disabled) disabled=true;
}
for(int i=0;i<exp.robots.Count;i++) {
double minx=1000,miny=1000,maxx=-1000,maxy=-1000;
for(int j=0;j<samples;j++) {
if(xc[i,j]<minx) minx=xc[i,j];
if(xc[i,j]>maxx) maxx=xc[i,j];
if(yc[i,j]<miny) miny=yc[i,j];
if(yc[i,j]>maxy) maxy=yc[i,j];
}
disabled=false;//disable for now...
if(disabled) {
minx *= -0.1;
maxx *= -0.1;
miny *= -0.1;
maxy *= -0.1;
}
//bc.Add(minx);
bc.Add(miny);
//bc.Add(maxx);
bc.Add(maxy);
}
// Console.WriteLine(bc.Count.ToString());
return new List<double>(bc);
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
objectives = new double[6];
objectives[0] = (accum + 2.5 * stopaccum) / 1000.0;
//objectives[0]=accum;
//objectives[1]=stopaccum;
double travel = 0.0;
double delta = 0.0;
double sight = 0.0;
foreach (Robot r in ip.robots)
{
delta += ((Khepera3RobotModel)r).totDelta; //r.collisions; //sensorList[i];//engine.robots[i].collisions;
sight += ((Khepera3RobotModel)r).totSight;
travel += r.dist_trav;
}
objectives[1] = -delta; // + -sight; //-Math.Log(delta+1.0)/10.0;
//objectives[2]= -Math.Log(sight+1.0)/10.0;
//objectives[1]= -coll_count;
//objectives[1]=travel;
//Console.WriteLine(ip.robots.Count);
double fitness = (accum + 3.5 * stopaccum) * (3.0 * (ip.robots.Count - 3)) - delta * 20 + 0.0001;
return(fitness);
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
double fitness = 0.0f;
//fitness += bonus;
for (int i = 0; i < reachedPOI.Length; i++)
{
if (reachedPOI[i])
{
fitness += 1.0f;
}
else
{
fitness += (1.0f - ip.robots[0].location.distance(new Point2D(environment.POIPosition[i].X, environment.POIPosition[i].Y)) / 650.0f);
break;
}
}
if (reachedGoal)
{
fitness = 10.0f;
}
objectives = null;
return fitness;
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
double fitness = 0.0f;
objectives = null;
// Only acknowledge actual eating of food, not just proximity
fitness = collectedFood / 4.0f;
if (collectedFood >= 4)
{
fitness = 1.0f;
}
// Schrum
// HACK: Both environments are the same, but this hack allows one to treat the food as poison
bool poison = !environment.name.Equals("ENV_dual_task1.xml");
// Extra aspect of the HACK: The first task loaded excludes its path from the name, but this is not
// the case for the second task loaded. This is why the negation is used instead of looking up the second
// task directly, which is named ENV_dual_task11.xml (convention of simulator)
if (poison)
{
fitness *= -0.9;
}
// Schrum: For troubleshooting
//Console.WriteLine(environment.name + " " + fitness + " " + poison);
return(fitness);
}
public void update(SimulatorExperiment engine, Environment environment)
{
if (first)
{
distances = new double[environment.POIPosition.Count, engine.robots.Count];
for (int v = 0; v < environment.POIPosition.Count; v++)
{
for (int w = 0; w < engine.robots.Count; w++)
{
distances[v, w] = radius;
}
}
first = false;
return;
}
double dist = 0;
for (int k = 0; k < engine.robots.Count; k++)
{
Robot r = engine.robots[k];
for (int j = 0; j < environment.POIPosition.Count; j++)
{
dist = EngineUtilities.euclideanDistance(r.location, new Point2D(environment.POIPosition[j].X, environment.POIPosition[j].Y));
if (dist < distances[j, k])
{
distances[j, k] = dist;
}
}
}
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment,instance_pack ip)
{
if (!(Experiment.timeSteps % (int)(1 / Experiment.timestep) == 0))
{
//grid.decay_viewed(0);
return;
}
foreach(Robot r in ip.robots) {
if (!r.autopilot) {
foreach(ISensor s in r.sensors)
if(s is SignalSensor) {
SignalSensor ss = (SignalSensor)s;
double val = ss.get_value();
val+=0.05;
if(val>1.0) val=1.0;
ss.setSignal(val);
}
}
}
double x1=(double)environment.AOIRectangle.Left;
double y1=(double)environment.AOIRectangle.Top;
double x2=(double)environment.AOIRectangle.Right;
double y2=(double)environment.AOIRectangle.Bottom;
int steps=10;
accum+=test_interpolation(ip,x1,y1,x2,y1,steps);
accum+=test_interpolation(ip,x2,y1,x2,y2,steps);
accum+=test_interpolation(ip,x2,y2,x2,y1,steps);
accum+=test_interpolation(ip,x2,y1,x1,y1,steps);
}
/*
* //Loads a environment from an XML file and initializes it
* public SimulatorExperiment load(string name)
* {
* System.Xml.Serialization.XmlSerializer x = new System.Xml.Serialization.XmlSerializer(typeof(SimulatorExperiment));
* TextReader infile = new StreamReader(name);
* SimulatorExperiment e = (SimulatorExperiment)x.Deserialize(infile);
* infile.Close();
*
* e.substrateDescription = new SubstrateDescription(e.substrateDescriptionFilename);
*
* e.fitnessFunction = FitnessFunctionFactory.getFitnessFunction(e.fitnessFunctionName);
*
* loadEnvironments(e);
*
* return e;
* }*/
public static void loadEnvironments(SimulatorExperiment e)
{
//Only reload environment if it isn't loaded yet, otherwise changes to the environment are lost
//TODO make sure
if (e.environmentList.Count == 0)
{
//e.environmentList.Clear();
Engine.Environment scene = Engine.Environment.loadEnvironment(e.environmentName);
e.environmentList.Add(scene);
e.environment = scene;
Console.Write("Looking for additional environments [" + scene.name + "] ... ");
String filenamePrefix = scene.name.Substring(0, scene.name.Length - 4);
int num = 1;
String filename2 = filenamePrefix + num + ".xml";
while (File.Exists(filename2))
{
Console.WriteLine("Found environment: " + filename2 + "\n");
e.environmentList.Add(Engine.Environment.loadEnvironment(filename2));
num++;
filename2 = filenamePrefix + num + ".xml";
}
Console.WriteLine("Done");
Console.WriteLine(e.environmentList.Count.ToString() + " environment(s) found.");
}
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
double fitness = 0.0f;
//fitness += bonus;
for (int i = 0; i < reachedPOI.Length; i++)
{
if (reachedPOI[i])
{
fitness += 1.0f;
}
else
{
fitness += (1.0f - ip.robots[0].location.distance(new Point2D(environment.POIPosition[i].X, environment.POIPosition[i].Y)) / 650.0f);
break;
}
}
if (reachedGoal)
{
fitness = 10.0f;
}
objectives = null;
return(fitness);
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
double fitness = 0.0f;
objectives = null;
// ENV_dual_task.xml is the hallway navigation environment of the dual task
// FourTasks-ENV2.xml is a direct copy of ENV_dual_task.xml, but the copy is
// required to satisfy the way that the simulator runs multiple environments.
if (environment.name.EndsWith("ENV_dual_task.xml") || environment.name.EndsWith("FourTasks-ENV2.xml")) //HACK navigation
{
fitness = (1.0f - ip.robots[0].location.distance(new Point2D(environment.POIPosition[4].X, environment.POIPosition[4].Y)) / 650.0f);
if (fitness < 0) fitness = 0.00001f;
if (reachedGoal) fitness = 1;
// fitness = 1;
}
else //food gathering
{
float distFood = (float)(1.0f - (engine.robots[0].location.distance(environment.goal_point) / environment.maxDistance));
fitness = (collectedFood + distFood) / 4.0f;
if (fitness < 0) fitness = 0.00001f;
if (collectedFood >= 4) fitness = 1.0f;
// fitness = 1;
}
return fitness;
}
public POIFIT_MO()
{
first = false;
const int maxsamples = 100;
const int maxrobots = 10;
reachGoal = new bool[maxrobots];
travelList = new List <int> [maxrobots];
for (int i = 0; i < maxrobots; i++)
{
travelList[i] = new List <int>();
}
currentLocation = new int[maxrobots];
endList = new double[maxrobots];
gotoList = new double[maxrobots];
origDist = new double[maxrobots];
origHeadings = new double[maxrobots];
turned = new bool[maxrobots];
reachList = new bool[maxrobots];
takenList = new int[maxrobots];
sensorList = new double[maxrobots];
theengine = null;
allClose = false;
allCorrected = false;
this.reset0();
}
void IFitnessFunction.update(SimulatorExperiment simExp, Environment environment, instance_pack ip)
{
// For brain switching by multitask.
// Schrum: If not using preference neurons, and the current brain does not match the environment
if (simExp.multibrain && !simExp.preferenceNeurons && ip.agentBrain.getBrainCounter() != currentEnvironment)
{
// Schrum: get the appropriate brain for this environment
ip.agentBrain.switchBrains(currentEnvironment);
}
if (currentEnvironment == TASK_TEAM_PATROL)
{ // Team patrol
teamPatrol.update(simExp, environment, ip);
}
else if (currentEnvironment == TASK_LONE_PATROL)
{ // Lone patrol
lonePatrol.update(simExp, environment, ip);
}
else if (currentEnvironment == TASK_DUAL_TASK_HALLWAY || currentEnvironment == TASK_DUAL_TASK_FORAGE)
{ // Dual task
dualTask.update(simExp, environment, ip);
}
else if (currentEnvironment == TASK_TWO_ROOMS)
{ // Two rooms
twoRooms.update(simExp, environment, ip);
}
else
{
Console.WriteLine("Error! Unknown environment! " + environment.name + ":" + currentEnvironment);
System.Environment.Exit(1);
}
}
//characterizing behavior...
List <double> IBehaviorCharacterization.calculate(SimulatorExperiment exp)
{
double mult = 1.0;
bool disabled = false;
for (int i = 0; i < exp.robots.Count; i++)
{
if (exp.robots[i].disabled)
{
disabled = true;
}
}
if (disabled)
{
mult = -0.1;
}
for (int x = 0; x < dim; x++)
{
for (int y = 0; y < dim; y++)
{
bc.Add((grid.grid[x, y].avg_idle + 1) / 10000.0 * mult);
}
}
// Console.WriteLine(bc.Count.ToString());
return(new List <double>(bc));
}
public void update(SimulatorExperiment engine, Environment environment)
{
if (first)
{
distances = new double[environment.POIPosition.Count, engine.robots.Count];
for (int v = 0; v < environment.POIPosition.Count; v++)
for (int w = 0; w < engine.robots.Count; w++)
distances[v, w] = radius;
first = false;
return;
}
double dist = 0;
for(int k=0;k<engine.robots.Count;k++)
{
Robot r = engine.robots[k];
for (int j = 0; j < environment.POIPosition.Count; j++)
{
dist = EngineUtilities.euclideanDistance(r.location, new Point2D(environment.POIPosition[j].X, environment.POIPosition[j].Y));
if (dist < distances[j, k])
distances[j, k] = dist;
}
}
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
objectives = new double[6];
double trackingFitness = 0.0f;
if (avgLoc == null) return 1;
/*
for (int i = 0; i < reachedPOI.Length; i++)
{
if (reachedPOI[i])
trackingFitness += 1.0f;
else
{
double dist = avgLoc.distance(new Point2D((int)environment.POIPosition[i].X, (int)environment.POIPosition[i].Y));
trackingFitness += ((1.0f - (dist / environment.maxDistance)) * 0.5);
break;
}
}*/
if (reachedGoal)
{
trackingFitness = 10.0f;
}
else
{
double dist = avgLoc.distance(environment.goal_point);
trackingFitness += ((1.0f - (dist / environment.maxDistance)) * 0.5);
}
objectives[0] = trackingFitness;
objectives[1] = inFormation;
if (formHeight == 0.0) formHeight = 0.00001;
return trackingFitness*2 + inFormation*.35 + (10.0/formHeight);
}
public static double MAX_LONE_PATROL = 6000; // Observed in GECCO 2016 work
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
if (currentEnvironment == TASK_TEAM_PATROL)
{ // Team patrol
// Must normalize score
return(teamPatrol.calculate(engine, environment, ip, out objectives) / MAX_TEAM_PATROL);
}
else if (currentEnvironment == TASK_LONE_PATROL)
{ // Lone patrol
// Must normalize score
return(lonePatrol.calculate(engine, environment, ip, out objectives) / MAX_LONE_PATROL);
}
else if (currentEnvironment == TASK_DUAL_TASK_HALLWAY || currentEnvironment == TASK_DUAL_TASK_FORAGE)
{ // Dual task
// Both individual dual task fitnss scores are already normalized
return(dualTask.calculate(engine, environment, ip, out objectives));
}
else if (currentEnvironment == TASK_TWO_ROOMS)
{ // Two rooms
// Score is already normalized to [0,1]
return(twoRooms.calculate(engine, environment, ip, out objectives));
}
else
{
Console.WriteLine("Error! Unknown environment! " + environment.name + ":" + currentEnvironment);
objectives = new double[0];
System.Environment.Exit(1);
return(-1000);
}
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] obj)
{
obj = new double[6];
fitness = 0.000001;
double go_sum = 1.0;
double ret_sum = 1.0;
double collide_count = 0;
bool moved = true;
//Checks to see if the robots have moved or turned since the signal has fired, meaning that they reacted to the signal
for (int j = 0; j < ip.robots.Count; j++)
if (turned[j] || origDist[j] - ip.robots[j].location.manhattenDistance(environment.goal_point) >= 5)
{
continue;
}
else
{
moved = false;
break;
}
if (!penalizeGettingToPoints)
allClose = true;
bool solve=true;
double temp;
if(!allClose || !moved) solve=false;
for (int i = 0; i < ip.robots.Count; i++)
{
if(!reachGoal[i]) solve=false;
if ((allClose && moved) || !penalizeSignalResponse)
fitness += gotoList[i];
else
fitness += gotoList[i] / 10.0;
temp = endList[i];
//if ((penalizeCorrection && !allCorrected) || (penalizeGettingToPoints && !allClose) || (penalizeSignalResponse && !moved))
if (penalizeCorrection && (!allCorrected || !allClose))
temp /= 100;
else if (penalizeGettingToPoints && !allClose)
temp /= 100;
//if(penalizeGettingToPoints && !allClose)
// temp/=100;
//if(penalizeSignalResponse && !moved)
// temp/=10;
fitness+= temp;
//Console.WriteLine(gotoList[i] + " " + endList[i]);
go_sum *= (gotoList[i] + 0.01);
ret_sum *= (endList[i] + 0.01);
obj[i * 2] = 0; //gotoList[i];
obj[i * 2 + 1] = 0; //endList[i];
collide_count += ip.robots[i].collisions; //sensorList[i];//engine.robots[i].collisions;
}
obj[0] = go_sum;
obj[1] = ret_sum;
obj[2] = -collide_count;
if(solve) fitness+=100.0;
return fitness;
}
public double calculate(SimulatorExperiment engine, Environment environment, out double[] obj)
{
obj=null;
if (first)
return 0;
double fit = 0;
double one = radius, two = radius;
for (int j = 0; j < environment.POIPosition.Count; j++)
{
one = radius;
two = radius;
for (int k = 0; k < engine.robots.Count; k++)
{
if (distances[j, k] < radius)
{
if (distances[j, k] < one)
{
two = one;
one = distances[j, k];
}
else if (distances[j, k] < two)
two = distances[j, k];
}
}
if (one != radius && two != radius)
fit += radius - ((one + two) / 2.0);
else if (one != radius)
fit += (radius - one) / 10.0;
}
return Math.Max(fit,0.001);
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
foreach (Robot r in ip.robots)
{
double dx = r.location.x - r.old_location.x;
latDist += dx;
}
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
objectives = null;
if (latDist < 0) return 0;
return latDist;
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
// Initialize variables on first time step
if (ip.timeSteps == 1)
{
livingCost = 0;
rewards = 0;
// Fitness values must be positive. Therefore, a high positive fitness is assigned in advance,
// and the cost of living subtracts from it.
// time / steps is the number of actual time steps, and the cost is multiplied by number of enemies
fitness = (Experiment.evaluationTime / Experiment.timestep) * Experiment.numEnemies;
}
// Find closest active prey
bool allCaptured = true; // becomes false if an active prey is found
Robot evolved = ip.robots[0];
for (int i = 1; i < ip.robots.Count; i++)
{
// Assumes all but first robot are EnemyRobot instances
EnemyRobot er = (EnemyRobot)ip.robots[i];
if (!er.disabled) // Not captured yet
{
//Console.WriteLine("Robot "+i+" not disabled");
allCaptured = false;
// The collisionWithEvolved bool should always be the primary means of detecting these
// collisions, but the other call is here as a precaution. This check is needed because
// when the evolved bot normally collides with the enemy, the "undo" method is called,
// which prevents the collision from being detected using normal means in this method.
// Fortunately, the collisionWithEvolved member is used to remember when this collision
// occurs.
if (er.collisionWithEvolved || EngineUtilities.collide(evolved, er))
{ // Reward evolved bot for colliding with prey, and disable prey
er.disabled = true;
er.stopped = true;
rewards += PREY_REWARD;
fitness += PREY_REWARD; // This is the value that matters
//Console.WriteLine("\treward:" + rewards + " from " + PREY_REWARD);
}
else
{ // Each active prey punishes bot for not being caltured yet
double distance = evolved.location.distance(er.location);
double cost = distance / environment.maxDistance;
livingCost += cost;
fitness -= cost; // This is the value that matters
//Console.WriteLine("\tCost: " + (distance / 1000.0) + " to be " + livingCost + " raw distance: " + distance);
}
}
}
// End evaluation and stop accruing negative fitness if all prey are captured
if (allCaptured)
{ // Disabling prevents further action
ip.elapsed = Experiment.evaluationTime; // force end time: only affects non-visual evaluation
Experiment.running = false; // Ends visual evaluation
}
}
//characterizing behavior...
List<double> IBehaviorCharacterization.calculate(SimulatorExperiment exp,instance_pack i)
{
// Console.WriteLine(bc.Count.ToString());
double[] obj;
//TODO: fix eventually;
//bc[0]=exp.fitnessFunction.calculate(exp,exp.environment,out obj);
bc[0]=0.0;
return new List<double>(bc);
}
//characterizing behavior...
List <double> IBehaviorCharacterization.calculate(SimulatorExperiment exp, instance_pack i)
{
// Console.WriteLine(bc.Count.ToString());
double[] obj;
//TODO: fix eventually;
//bc[0]=exp.fitnessFunction.calculate(exp,exp.environment,out obj);
bc[0] = 0.0;
return(new List <double>(bc));
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
// Initialize variables on first time step
if (ip.timeSteps == 1)
{
livingCost = 0;
rewards = 0;
// Fitness values must be positive. Therefore, a high positive fitness is assigned in advance,
// and the cost of living subtracts from it.
// time / steps is the number of actual time steps, and the cost is multiplied by number of enemies
fitness = (Experiment.evaluationTime / Experiment.timestep) * Experiment.numEnemies;
}
// Find closest active prey
bool allCaptured = true; // becomes false if an active prey is found
Robot evolved = ip.robots[0];
for (int i = 1; i < ip.robots.Count; i++)
{
// Assumes all but first robot are EnemyRobot instances
EnemyRobot er = (EnemyRobot)ip.robots[i];
if (!er.disabled) // Not captured yet
{
//Console.WriteLine("Robot "+i+" not disabled");
allCaptured = false;
// The collisionWithEvolved bool should always be the primary means of detecting these
// collisions, but the other call is here as a precaution. This check is needed because
// when the evolved bot normally collides with the enemy, the "undo" method is called,
// which prevents the collision from being detected using normal means in this method.
// Fortunately, the collisionWithEvolved member is used to remember when this collision
// occurs.
if (er.collisionWithEvolved || EngineUtilities.collide(evolved, er))
{ // Reward evolved bot for colliding with prey, and disable prey
er.disabled = true;
er.stopped = true;
rewards += PREY_REWARD;
fitness += PREY_REWARD; // This is the value that matters
//Console.WriteLine("\treward:" + rewards + " from " + PREY_REWARD);
}
else
{ // Each active prey punishes bot for not being caltured yet
double distance = evolved.location.distance(er.location);
double cost = distance / environment.maxDistance;
livingCost += cost;
fitness -= cost; // This is the value that matters
//Console.WriteLine("\tCost: " + (distance / 1000.0) + " to be " + livingCost + " raw distance: " + distance);
}
}
}
// End evaluation and stop accruing negative fitness if all prey are captured
if (allCaptured)
{ // Disabling prevents further action
ip.elapsed = Experiment.evaluationTime; // force end time: only affects non-visual evaluation
Experiment.running = false; // Ends visual evaluation
}
}
public override void Initialize(Environment e, SimulatorExperiment _exp, List <Robot> _rbts)
{
rbts = _rbts;
exp = _exp;
if (_exp is MultiAgentExperiment)
{
agentCollide = ((MultiAgentExperiment)exp).agentsCollide;
agentVisible = ((MultiAgentExperiment)exp).agentsVisible;
}
env = e;
}
public void onTimeStep(SimulatorExperiment experiment)
{
//Disabled for now
//TODO include again
//tsteps++;
//if(!all_in && eval.autopilot_count==0)
//{
// eval.sim_engine.find_wall_by_name("door").visible=true;
// all_in=true;
//}
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
if (!(ip.timeSteps % (int)(1 / ip.timestep) == 0))
{
//grid.decay_viewed(0);
return;
}
bool all_in = true;
double a_accum = 0.00000001;
double a_saccum = 0.00000001;
foreach (Robot r in ip.robots)
{
if (!r.autopilot)
{
foreach (ISensor s in r.sensors)
{
if (s is SignalSensor)
{
SignalSensor ss = (SignalSensor)s;
double val = ss.get_value();
val += 0.05;
if (val > 1.0)
{
val = 1.0;
}
ss.setSignal(val);
}
}
}
if ((environment.AOIRectangle.Contains((int)r.location.x, (int)r.location.y)))
{
a_accum += 1.0 / (nearest(ip, r, environment));
if (r.corrected)
{
a_saccum += nearest(ip, r, environment);
}
//else
// a_saccum+=1.0;
}
else
{
all_in = false;
}
}
if (all_in)
{
accum += ((double)ip.robots.Count) / (a_accum);
stopaccum += a_saccum / ((double)ip.robots.Count);
}
}
public void onTimeStep(SimulatorExperiment experiment)
{
//Disabled for now
//TODO include again
//tsteps++;
//if(!all_in && eval.autopilot_count==0)
//{
// eval.sim_engine.find_wall_by_name("door").visible=true;
// all_in=true;
//}
}
List<double> IBehaviorCharacterization.calculate(SimulatorExperiment engine)
{
Environment environment = engine.environment;
//bc.Clear();
for(int i=0;i<engine.robots.Count;i++)
{
//bc.Add(gotoList[i]);
//bc.Add(endList[i]);
}
return new List<double>(bc);
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment,out double[] objectives)
{
objectives=null;
double[] pmax=new double[3];
double[] prog=new double[3];
double fitness=1.0;
pmax[0]=0;
pmax[1]=0;
pmax[2]=0;
for(int i=0;i<engine.robots.Count;i++) {
int up_progress= -1;
int up_type=0;
int down_progress= 1000;
double progress= 0;
bool debug=false;
if(travelList[i].Count==0) continue;
int up_ind=0;
for(int z=0;z<travelList[i].Count;z++) {
int d=travelList[i][z];
if (debug) Console.Write(d+" ");
if ((d%3)>up_progress) {
up_progress=d%3;
up_ind=z;
up_type=d/3;
}
}
if(debug) Console.WriteLine();
for(int z=up_ind;z<travelList[i].Count;z++) {
int d=travelList[i][z];
if ((d%3)<down_progress && (up_type==d/3)) {
down_progress=d%3;
}
}
progress= (up_progress+1) + (up_progress - down_progress);
//if(finishList[i]==1)
progress+=finishList[i]*10.0;
prog[i]=progress;
if(progress>pmax[up_type])
pmax[up_type]=progress;
}
double limit=Math.Min(Math.Min(prog[0],prog[1]),prog[2]);
double differential=Math.Max(Math.Max(prog[0]-limit,prog[1]-limit),prog[2]-limit);
for(int i=0;i<3;i++) {
fitness+=pmax[i]*5;
fitness+=prog[i];
fitness-=differential*2;
}
return Math.Max(0.00001,fitness);
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment,instance_pack ip,out double[] objectives)
{
objectives=new double[6];
objectives[0]=accum;
double travel=0.0;
foreach(Robot r in ip.robots) {
travel+=r.dist_trav;
coll_count+=r.collisions;
}
objectives[1]= stop_accum; //-collisions;
return accum+stop_accum*2.0;
}
public override void Initialize (Environment e,SimulatorExperiment _exp,List<Robot> _rbts)
{
rbts = _rbts;
exp=_exp;
if (_exp is MultiAgentExperiment)
{
agentCollide = ((MultiAgentExperiment)exp).agentsCollide;
agentVisible = ((MultiAgentExperiment)exp).agentsVisible;
}
env = e;
}
//characterizing behavior...
List<double> IBehaviorCharacterization.calculate(SimulatorExperiment exp)
{
bool disabled=false;
for(int i=0;i<exp.robots.Count;i++) {
if(exp.robots[i].disabled) disabled=true;
}
int count = samples;
for(int i=0;i<exp.robots.Count;i++) {
double sumx=0.0,sumsqx=0.0,sumy=0.0,sumsqy=0.0;
for(int j=0;j<samples;j++) {
double x=xc[i,j];
double y=yc[i,j];
sumx+=x;
sumsqx+=(x*x);
sumy+=y;
sumsqy+=(y*y);
}
double meanx=sumx/count;
double meansqx=sumsqx/count;
double meany=sumy/count;
double meansqy=sumsqy/count;
double varx = meansqx-(meanx*meanx);
double vary = meansqy-(meany*meany);
if(varx<0) varx=0;
if(vary<0) vary=0;
double stdx=Math.Sqrt(varx);
double stdy=Math.Sqrt(vary);
double disable_mult=1.0;
if(disabled) {
disable_mult= -0.1;
}
double[] list={meanx*disable_mult,meany*disable_mult,stdx*disable_mult,stdy*disable_mult};
int index=0;
for(index=0;index<bc.Count;index+=4) {
if(!disabled &&list[0] < bc[index])
break;
else if(disabled&&list[0] >bc[index])
break;
}
bc.InsertRange(index,list);
}
/*
for(int i=0;i<bc.Count;i++)
Console.Write(bc[i].ToString()+ " ");
Console.WriteLine();
*/
return new List<double>(bc);
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
// HACK: Both environments are the same, but this hack allows one to treat the food as poison
bool poison = !environment.name.Equals("ENV_dual_task1.xml");
if (Experiment.multibrain && !Experiment.preferenceNeurons && Experiment.numBrains == 2)
{
if (!poison) //forage
{
ip.agentBrain.switchBrains(0);
}
else //poison
{
ip.agentBrain.switchBrains(1);
}
}
//For food gathering
if (ip.timeSteps == 1)
{
environment.goal_point.x = environment.POIPosition[0].X;
environment.goal_point.y = environment.POIPosition[0].Y;
collectedFood = 0;
POINr = 0;
}
// Schrum: Last sensor is for detecting when food/poison is eaten
Robot r = ip.robots[0]; // There should be only one robot in this task
SignalSensor s = (SignalSensor)r.sensors[r.sensors.Count - 1];
float d = (float)ip.robots[0].location.distance(environment.goal_point);
if (d < 20.0f)
{
// Need to detect when food or poison is eaten
s.setSignal(poison ? -1.0 : 1.0);
collectedFood++;
POINr++;
if (POINr > 3)
{
POINr = 0;
}
environment.goal_point.x = environment.POIPosition[POINr].X;
environment.goal_point.y = environment.POIPosition[POINr].Y;
}
else
{// Nothing eaten, so food/poison sensor is 0
s.setSignal(0.0);
}
}
void IBehaviorCharacterization.update(SimulatorExperiment exp)
{
grid = ((GridCollision)((MultiAgentExperiment)exp).collisionManager).grid;
dim = grid.coarseness;
if (exp.timeSteps <=1)
{
for (int x = 0; x < dim; x++)
{
for (int y = 0; y < dim; y++)
{
int gx = (int)((double)x * grid.gridx) + (int)(grid.gridx / 2.0);
int gy = (int)((double)y * grid.gridy) + (int)(grid.gridy / 2.0);
grid.grid[x, y].viewed = 0.0f;
grid.grid[x, y].idleness = 0.0f;
grid.grid[x, y].avg_idle=0.0f;
}
}
}
//if(!(Experiment.timeSteps % 5 ==0))
//{
// grid.decay_viewed(0.3);
// return;
//}
for (int x = 0; x < dim; x++)
{
for (int y = 0; y < dim; y++)
{
int gx = (int)((double)x * grid.gridx) + (int)(grid.gridx / 2.0);
int gy = (int)((double)y * grid.gridy) + (int)(grid.gridy / 2.0);
if ((exp.environment.AOIRectangle.Contains(gx, gy)))
{
if (grid.grid[x, y].viewed>=0.95f)
{
grid.grid[x, y].idleness = 0.0f;
}
else
{
if (grid.grid[x, y].idleness<255)
grid.grid[x, y].idleness += 1.0f;
grid.grid[x,y].avg_idle += grid.grid[x, y].idleness;
}
//accum +=
}
}
}
}
List <double> IBehaviorCharacterization.calculate(SimulatorExperiment engine)
{
Environment environment = engine.environment;
//bc.Clear();
for (int i = 0; i < engine.robots.Count; i++)
{
//bc.Add(gotoList[i]);
//bc.Add(endList[i]);
}
return(new List <double>(bc));
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment,out double[] obj)
{
obj=null;
fitness=0.000001;
for(int i=0;i<engine.robots.Count;i++)
{
fitness+=gotoList[i];
fitness+=endList[i];
}
return fitness;
//return Math.Max(0.00001,fitness);
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, out double[] obj)
{
obj = null;
fitness = 0.000001;
for (int i = 0; i < engine.robots.Count; i++)
{
fitness += gotoList[i];
fitness += endList[i];
}
return(fitness);
//return Math.Max(0.00001,fitness);
}
void IBehaviorCharacterization.update(SimulatorExperiment exp)
{
grid = ((GridCollision)((MultiAgentExperiment)exp).collisionManager).grid;
dim = grid.coarseness;
if (exp.timeSteps <= 1)
{
for (int x = 0; x < dim; x++)
{
for (int y = 0; y < dim; y++)
{
int gx = (int)((double)x * grid.gridx) + (int)(grid.gridx / 2.0);
int gy = (int)((double)y * grid.gridy) + (int)(grid.gridy / 2.0);
grid.grid[x, y].viewed = 0.0f;
grid.grid[x, y].idleness = 0.0f;
grid.grid[x, y].avg_idle = 0.0f;
}
}
}
//if(!(Experiment.timeSteps % 5 ==0))
//{
// grid.decay_viewed(0.3);
// return;
//}
for (int x = 0; x < dim; x++)
{
for (int y = 0; y < dim; y++)
{
int gx = (int)((double)x * grid.gridx) + (int)(grid.gridx / 2.0);
int gy = (int)((double)y * grid.gridy) + (int)(grid.gridy / 2.0);
if ((exp.environment.AOIRectangle.Contains(gx, gy)))
{
if (grid.grid[x, y].viewed >= 0.95f)
{
grid.grid[x, y].idleness = 0.0f;
}
else
{
if (grid.grid[x, y].idleness < 255)
{
grid.grid[x, y].idleness += 1.0f;
}
grid.grid[x, y].avg_idle += grid.grid[x, y].idleness;
}
//accum +=
}
}
}
}
//characterizing behavior...
List <double> IBehaviorCharacterization.calculate(SimulatorExperiment exp)
{
bool disabled = false;
for (int i = 0; i < exp.robots.Count; i++)
{
if (exp.robots[i].disabled)
{
disabled = true;
}
}
for (int i = 0; i < exp.robots.Count; i++)
{
double minx = 1000, miny = 1000, maxx = -1000, maxy = -1000;
for (int j = 0; j < samples; j++)
{
if (xc[i, j] < minx)
{
minx = xc[i, j];
}
if (xc[i, j] > maxx)
{
maxx = xc[i, j];
}
if (yc[i, j] < miny)
{
miny = yc[i, j];
}
if (yc[i, j] > maxy)
{
maxy = yc[i, j];
}
}
disabled = false; //disable for now...
if (disabled)
{
minx *= -0.1;
maxx *= -0.1;
miny *= -0.1;
maxy *= -0.1;
}
//bc.Add(minx);
bc.Add(miny);
//bc.Add(maxx);
bc.Add(maxy);
}
// Console.WriteLine(bc.Count.ToString());
return(new List <double>(bc));
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
//Console.WriteLine(Experiment.multibrain + " && " + !Experiment.preferenceNeurons + " && " + (Experiment.numBrains == 2));
// Schrum: Set which brain to use if the number is an experiment parameter
// Schrum: Because the explicit brain switching only occurs when numBrains == 2, it will not register in FourTasks experiments using 5 brains
if (Experiment.multibrain && !Experiment.preferenceNeurons && Experiment.numBrains == 2)
{
if (environment.name.EndsWith("ENV_dual_task.xml") || environment.name.EndsWith("FourTasks-ENV2.xml")) //HACK navigation
{
ip.agentBrain.switchBrains(0);
}
else //food gathering
{
ip.agentBrain.switchBrains(1);
}
}
//For navigation
if (ip.robots[0].location.distance(new Point2D((int)environment.POIPosition[4].X, (int)environment.POIPosition[4].Y)) < 20.0f)
{
reachedGoal = true;
}
//For food gathering
if (ip.timeSteps == 1)
{
environment.goal_point.x = environment.POIPosition[0].X;
environment.goal_point.y = environment.POIPosition[0].Y;
collectedFood = 0;
POINr = 0;
}
float d = (float)ip.robots[0].location.distance(environment.goal_point);
if (d < 20.0f)
{
collectedFood++;
POINr++;
if (POINr > 3)
{
POINr = 0;
}
environment.goal_point.x = environment.POIPosition[POINr].X;
environment.goal_point.y = environment.POIPosition[POINr].Y;
}
//Console.WriteLine("reachedGoal = " + reachedGoal + ", d = " + d + ", goal = " + environment.goal_point);
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
// HACK: Both environments are the same, but this hack allows one to treat the food as poison
bool poison = !environment.name.Equals("ENV_dual_task1.xml");
if (Experiment.multibrain && !Experiment.preferenceNeurons && Experiment.numBrains == 2)
{
if (!poison) //forage
{
ip.agentBrain.switchBrains(0);
}
else //poison
{
ip.agentBrain.switchBrains(1);
}
}
//For food gathering
if (ip.timeSteps == 1)
{
environment.goal_point.x = environment.POIPosition[0].X;
environment.goal_point.y = environment.POIPosition[0].Y;
collectedFood = 0;
POINr = 0;
}
// Schrum: Last sensor is for detecting when food/poison is eaten
Robot r = ip.robots[0]; // There should be only one robot in this task
SignalSensor s = (SignalSensor)r.sensors[r.sensors.Count - 1];
float d = (float)ip.robots[0].location.distance(environment.goal_point);
if (d < 20.0f)
{
// Need to detect when food or poison is eaten
s.setSignal(poison ? -1.0 : 1.0);
collectedFood++;
POINr++;
if (POINr > 3) POINr = 0;
environment.goal_point.x = environment.POIPosition[POINr].X;
environment.goal_point.y = environment.POIPosition[POINr].Y;
}
else
{// Nothing eaten, so food/poison sensor is 0
s.setSignal(0.0);
}
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
objectives = new double[6];
objectives[0] = accum;
double travel = 0.0;
foreach (Robot r in ip.robots)
{
travel += r.dist_trav;
coll_count += r.collisions;
}
objectives[1] = stop_accum; //-collisions;
return(accum + stop_accum * 2.0);
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment,instance_pack ip,out double[] objectives)
{
objectives=new double[6];
objectives[0]=100000.0/(accum+1.0);
objectives[0]*=1000.0;
double travel=0.0;
foreach(Robot r in ip.robots) {
coll_count += r.collisions; //sensorList[i];//engine.robots[i].collisions;
travel+=r.dist_trav;
}
objectives[1]= -coll_count;
//objectives[1]=travel;
return objectives[0];
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
grid = ((GridCollision)(ip.collisionManager)).grid;
if (!(ip.timeSteps % (int)(1 / Experiment.timestep) == 0))
{
return;
}
int dim = grid.coarseness;
for (int x = 0; x < dim; x++)
{
for (int y = 0; y < dim; y++)
{
int gx = (int)((double)x * grid.gridx) + (int)(grid.gridx / 2.0);
int gy = (int)((double)y * grid.gridy) + (int)(grid.gridy / 2.0);
if ((environment.AOIRectangle.Contains(gx, gy)))
{
accum += grid.grid[x, y].viewed;
stop_accum += grid.grid[x, y].viewed2;
}
}
}
foreach (Robot r in ip.robots)
{
if (!r.autopilot)
{
foreach (ISensor s in r.sensors)
{
if (s is SignalSensor)
{
SignalSensor ss = (SignalSensor)s;
double val = ss.get_value();
val += 0.05;
if (val > 1.0)
{
val = 1.0;
}
ss.setSignal(val);
}
}
}
}
grid.decay_viewed(0);
//grid.decay_viewed(.95);
}
List<double> IBehaviorCharacterization.calculate(SimulatorExperiment engine,instance_pack ip)
{
Environment environment = engine.environment;
bc.Clear();
double gotosum=1.0;
double endsum=1.0;
for(int i=0;i<ip.robots.Count;i++)
{
bc.Add(gotoList[i]);
bc.Add(endList[i]);
}
bc.Sort();
return new List<double>(bc);
}
//characterizing behavior...
List<double> IBehaviorCharacterization.calculate(SimulatorExperiment exp)
{
double mult=1.0;
bool disabled=false;
for(int i=0;i<exp.robots.Count;i++) {
if(exp.robots[i].disabled) disabled=true;
}
if(disabled) mult= -0.1;
for(int x=0;x<dim;x++)
for(int y=0;y<dim;y++)
bc.Add((grid.grid[x,y].avg_idle+1)/10000.0*mult);
// Console.WriteLine(bc.Count.ToString());
return new List<double>(bc);
}
//int i = 0;
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
// Schrum: Debug: For comparing non-visual eval with visual
// Prints out locations visited by all robots
/*
* for (int i = 0; i < ip.robots.Count; i++)
* {
* Console.Write(ip.robots[i].location.x + "\t" + ip.robots[i].location.y + "\t");
* if (ip.robots[i] is EnemyRobot)
* {
* Console.Write(((EnemyRobot)ip.robots[i]).wallResponse + "\t" + ((EnemyRobot)ip.robots[i]).chaseResponse + "\t" + ip.robots[i].heading + "\t" + ((EnemyRobot)ip.robots[i]).angle + "\t" + ip.robots[i].collisions + "\t");
* }
* }
* Console.WriteLine();
*/
/*
* if (ip.robots[0].location.x != ((EnemyRobot)ip.robots[1]).getEvolved().location.x || ip.robots[0].location.y != ((EnemyRobot)ip.robots[1]).getEvolved().location.y)
* {
* Console.WriteLine("Different locations:");
* Console.WriteLine("Robot 0: " + ip.robots[0].location);
* Console.WriteLine("Enemy's reference refr to evolved: " + ((EnemyRobot)ip.robots[1]).getEvolved().location);
* if (i++ > 5)
* {
* System.Windows.Forms.Application.Exit();
* System.Environment.Exit(1);
* }
* }
*/
if (ip.timeSteps == 1)
{
collided = false;
portionAlive = 0;
}
// Schrum2: Added to detect robot collisions and end the evaluation when they happen
if (ip.robots[0].collisions > 0)
{ // Disabling prevents further action
//Console.WriteLine("Collision");
collided = true;
ip.elapsed = Experiment.evaluationTime; // force end time: only affects non-visual evaluation
Experiment.running = false; // Ends visual evaluation
}
else
{
portionAlive++;
}
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
objectives = new double[6];
objectives[0] = 100000.0 / (accum + 1.0);
objectives[0] *= 1000.0;
double travel = 0.0;
foreach (Robot r in ip.robots)
{
coll_count += r.collisions; //sensorList[i];//engine.robots[i].collisions;
travel += r.dist_trav;
}
objectives[1] = -coll_count;
//objectives[1]=travel;
return(objectives[0]);
}
void IBehaviorCharacterization.update(SimulatorExperiment exp,instance_pack ip)
{
if(count%sample_rate==0) {
int rc=0;
foreach(Robot r in ip.robots) {
xc[rc,samples]=r.location.x;
yc[rc,samples]=r.location.y;
rc++;
}
//Console.WriteLine(samples);
samples++;
}
count++;
}
List <double> IBehaviorCharacterization.calculate(SimulatorExperiment engine, instance_pack ip)
{
Environment environment = engine.environment;
bc.Clear();
double gotosum = 1.0;
double endsum = 1.0;
for (int i = 0; i < ip.robots.Count; i++)
{
bc.Add(gotoList[i]);
bc.Add(endList[i]);
}
bc.Sort();
return(new List <double>(bc));
}
public override void Initialize(Environment e, SimulatorExperiment _exp, List <Robot> _rbts)
{
rbts = _rbts;
env = e;
exp = _exp;
if (_exp is MultiAgentExperiment)
{
agentCollide = ((MultiAgentExperiment)_exp).agentsCollide;
agentVisible = ((MultiAgentExperiment)_exp).agentsVisible;
}
grid = new collision_grid(e, coarseness);
foreach (Wall w in env.walls)
{
grid.insert_into_grid(w);
}
}
public override void Initialize (Environment e,SimulatorExperiment _exp,List<Robot> _rbts)
{
rbts=_rbts;
env=e;
exp=_exp;
if (_exp is MultiAgentExperiment)
{
agentCollide = ((MultiAgentExperiment)_exp).agentsCollide;
agentVisible = ((MultiAgentExperiment)_exp).agentsVisible;
}
grid = new collision_grid(e,coarseness);
foreach (Wall w in env.walls)
{
grid.insert_into_grid(w);
}
}
void IBehaviorCharacterization.update(SimulatorExperiment exp, instance_pack ip)
{
if (count % sample_rate == 0)
{
int rc = 0;
foreach (Robot r in ip.robots)
{
xc[rc, samples] = r.location.x;
yc[rc, samples] = r.location.y;
rc++;
}
//Console.WriteLine(samples);
samples++;
}
count++;
}
double IFitnessFunction.calculate(SimulatorExperiment engine, Environment environment, instance_pack ip, out double[] objectives)
{
objectives = new double[6];
double caughtFitness = preyCaught * 100.0;
objectives[0] = caughtFitness;
double timeFitness = 0;
if (finished)
timeFitness = (double)engine.evaluationTime - finishTime;
if (timeFitness < 0) timeFitness = 0.0;
objectives[1] = timeFitness;
//double travelFitness = traveled * .0002;
return caughtFitness + timeFitness * 2; // +travelFitness;
}
//int i = 0;
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
// Schrum: Debug: For comparing non-visual eval with visual
// Prints out locations visited by all robots
/*
for (int i = 0; i < ip.robots.Count; i++)
{
Console.Write(ip.robots[i].location.x + "\t" + ip.robots[i].location.y + "\t");
if (ip.robots[i] is EnemyRobot)
{
Console.Write(((EnemyRobot)ip.robots[i]).wallResponse + "\t" + ((EnemyRobot)ip.robots[i]).chaseResponse + "\t" + ip.robots[i].heading + "\t" + ((EnemyRobot)ip.robots[i]).angle + "\t" + ip.robots[i].collisions + "\t");
}
}
Console.WriteLine();
*/
/*
if (ip.robots[0].location.x != ((EnemyRobot)ip.robots[1]).getEvolved().location.x || ip.robots[0].location.y != ((EnemyRobot)ip.robots[1]).getEvolved().location.y)
{
Console.WriteLine("Different locations:");
Console.WriteLine("Robot 0: " + ip.robots[0].location);
Console.WriteLine("Enemy's reference refr to evolved: " + ((EnemyRobot)ip.robots[1]).getEvolved().location);
if (i++ > 5)
{
System.Windows.Forms.Application.Exit();
System.Environment.Exit(1);
}
}
*/
if (ip.timeSteps == 1)
{
collided = false;
portionAlive = 0;
}
// Schrum2: Added to detect robot collisions and end the evaluation when they happen
if (ip.robots[0].collisions > 0)
{ // Disabling prevents further action
//Console.WriteLine("Collision");
collided = true;
ip.elapsed = Experiment.evaluationTime; // force end time: only affects non-visual evaluation
Experiment.running = false; // Ends visual evaluation
}
else
{
portionAlive++;
}
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment, instance_pack ip)
{
incrementFitness(environment, ip);
bool all = true;
for (int i = 0; i < reachedPOI.Length; i++)
{
all = all && reachedPOI[i];
}
if (all)
{
return; // Schrum: Done if all goals were reached
}
for (int i = 0; i < environment.POIPosition.Count; i++)
{
if (reachedPOI[i])
{
continue; // Schrum: Once one POI has been reached, move to the next
}
else if (ip.robots[0].location.distance(new Point2D((int)environment.POIPosition[i].X, (int)environment.POIPosition[i].Y)) < 10.0f)
{
reachedPOI[i] = true;
// Schrum: Only manually change brains if preference neurons are not used
// Schrum: Don't switch brains here if there are 5 brains, since this corresponds to the FourTasks experiments.
if (Experiment.multibrain && !Experiment.preferenceNeurons && Experiment.numBrains != 5)
{
if (ip.agentBrain.numBrains == 3) // Schrum: Playing with special cases. Still need something more general.
{
int[] mapping = new int[] { 1, 2, 1, 0 }; // Mapping to the next module to use. Module 1 is repeated since it is for straight corridors.
ip.agentBrain.switchBrains(mapping[i]);
}
else
{ // Schrum: I'm not sure this option is actually used anywhere
ip.agentBrain.switchBrains(i + 1); // Schrum: Switch to next brain (one for each step of task)
}
}
}
break; // Schrum: Can't reach two at once, and must reach in order. Only "continue" can get past this
}
// Schrum: Once all POIs have been checked, the goal (returning) can be checked. Goal treated like extra POI
if (reachedPOI[2] && ip.robots[0].location.distance(environment.goal_point) < 10.0f)
{
reachedPOI[3] = true;
}
}
void IFitnessFunction.update(SimulatorExperiment Experiment, Environment environment)
{
if (!(Experiment.timeSteps % (int)(1 / Experiment.timestep) == 0))
{
//grid.decay_viewed(0);
return;
}
if (!allLeft)
{
bool allOut = true;
for (int j = 0; j < Experiment.robots.Count; j++)
{
if (!environment.AOIRectangle.Contains((int)Experiment.robots[j].location.x, (int)Experiment.robots[j].location.y))
{
allOut = false;
break;
}
}
if (allOut)
{
allLeft = true;
}
}
grid = ((GridCollision)((MultiAgentExperiment)Experiment).collisionManager).grid;
int dim = grid.coarseness;
for (int x = 0; x < dim; x++)
{
for (int y = 0; y < dim; y++)
{
int gx = (int)((double)x * grid.gridx) + (int)(grid.gridx / 2.0);
int gy = (int)((double)y * grid.gridy) + (int)(grid.gridy / 2.0);
if ((environment.AOIRectangle.Contains(gx, gy)))
{
accum += grid.grid[x, y].viewed;
}
}
}
//grid.decay_viewed(.9);
}