public void init(int id, double nx, double ny, double dir,
AgentBrain agentBrain, Environment environment,
float sensorNoise, float effectorNoise, float headingNoise, float timeStep)
{
steps = 0;
this.id = id;
radius = defaultRobotSize();
location = new Point2D(nx, ny);
circle = new Circle2D(location, radius);
old_location = new Point2D(location);
heading = dir;
velocity = 0.0;
collide_last = false;
this.timeStep = timeStep;
this.environment = environment;
this.agentBrain = agentBrain;
this.sensorNoise = sensorNoise;
this.effectorNoise = effectorNoise;
this.headingNoise = headingNoise;
populateSensors();
if (environment.seed != -1)
{
rng = environment.rng; //new SharpNeatLib.Maths.FastRandom(environment.seed); //Utilities.random;
}
else
{
rng = environment.rng; //new SharpNeatLib.Maths.FastRandom();
}
}
/// <summary>
/// Initialize the robot.
/// </summary>
public void init(int id, double locationX, double locationY, double heading, AgentBrain agentBrain, Environment environment, float sensorNoise, float effectorNoise, float headingNoise, float timeStep)
{
this.ID = id;
Radius = defaultRobotSize();
Location = new Point2D(locationX, locationY);
AreaOfImpact = new Circle2D(Location, Radius);
OldLocation = new Point2D(Location);
Heading = heading;
Velocity = 0.0;
HasCollided = false;
this.Timestep = timeStep;
this.CurrentEnvironment = environment;
this.Brain = agentBrain;
this.SensorNoise = sensorNoise;
this.EffectorNoise = effectorNoise;
this.HeadingNoise = headingNoise;
populateSensors();
if (environment.seed != -1)
{
rng = environment.rng;
}
else
{
rng = environment.rng;
}
this.Trajectory = new List <int>();
}
public void reset()
{
seed = 0;
view_x = 0.0f;
view_y = 0.0f;
view_scale = 5.0f;
AOIRectangle = new Rectangle(30, 60, 640, 500);
group_orientation = 0;
robot_spacing = 30;
robot_heading = 0;
rng = new SharpNeatLib.Maths.FastRandom();
walls = new List <Wall>();
POIPosition = new List <Point>();
start_point = new Point2D(0, 0);
goal_point = new Point2D(100, 100);
}
// Schrum: Had to remove the internal from this too so that I could override it in AdversarialRoomClearingExperiment
public override double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
// Schrum: Used when punishing links in substrate networks.
int linksInSubstrate = 0;
double fitness = multiplicativeFitness ? 1 : 0;
behavior = new SharpNeatLib.BehaviorType();
// Schrum: Why is there a magic number 6 here?
double[] accumObjectives = new double[6];
for (int i = 0; i < 6; i++)
{
accumObjectives[i] = 0.0;
}
// Schrum: Special handling for FourTasks domain again
if (fitnessFunction is FourTasksFitness)
{
// Pass the experiment into the fitness function once so its parameters can be changed for each environment
((FourTasksFitness)fitnessFunction).setExperiment(this);
}
IFitnessFunction fit_copy;
IBehaviorCharacterization bc_copy;
//CollisionManager cm;
int envNum = 0;
// Schrum: Separate fitness for each environment.
// Should this be put in accumObjectives instead? What is that for?
double[] environmentScores = new double[environmentList.Count];
foreach (Environment env2 in environmentList)
{
//Console.WriteLine("====================Environment loop " + envNum);
// Schrum: moved this here to deal with consistency issues
instance_pack inst = new instance_pack();
fit_copy = fitnessFunction.copy();
if (behaviorCharacterization != null)
{
bc_copy = behaviorCharacterization.copy();
inst.bc = bc_copy;
}
inst.ff = fit_copy;
// Schrum: Just add special handling for FourTasks to get settings right
if (inst.ff is FourTasksFitness)
{
// FourTasks needs to know the current environment. Experiment reference provided earlier.
((FourTasksFitness)inst.ff).setupFitness(envNum);
}
// Schrum: moved here from outside/before the loop. Most domains use the same timestep in each environment,
// but the FourTasks domain is an exception to this, so the setting needed to be moved after setupFitness
inst.timestep = timestep;
double tempFit = 0;
double[] fitnesses = new double[timesToRunEnvironments];
SharpNeatLib.Maths.FastRandom evalRand = new SharpNeatLib.Maths.FastRandom(100);
for (int evals = 0; evals < timesToRunEnvironments; evals++)
{
int agent_trials = timesToRunEnvironments;
inst.num_rbts = this.numberRobots;
Environment env = env2.copy();
double evalTime = this.evaluationTime;
inst.eval = evals;
env.seed = evals;
env.rng = new SharpNeatLib.Maths.FastRandom(env.seed + 1);
int noise_lev = (int)this.sensorNoise + 1;
float new_sn = evalRand.NextUInt() % noise_lev;
float new_ef = evalRand.NextUInt() % noise_lev;
inst.agentBrain = new AgentBrain(homogeneousTeam, inst.num_rbts, substrateDescription, network, normalizeWeights, adaptableANN, modulatoryANN, multibrain, numBrains, evolveSubstrate, preferenceNeurons, forcedSituationalPolicyGeometry);
// Add up the links in each substrate brain
// Recalculates each evaluation, but resets to 0 each time.
linksInSubstrate = 0;
foreach (INetwork b in inst.agentBrain.multiBrains)
{
linksInSubstrate += b.NumLinks;
}
if (eval)
{
Console.WriteLine("Links In Substrate: " + linksInSubstrate);
}
initializeRobots(inst.agentBrain, env, headingNoise, new_sn, new_ef, inst);
inst.elapsed = 0;
inst.timestep = this.timestep;
//Console.WriteLine(this.timestep);
inst.timeSteps = 0;
inst.collisionManager = collisionManager.copy();
inst.collisionManager.Initialize(env, this, inst.robots);
try
{
//Console.WriteLine("Environment " + environmentName + " " + envNum);
while (inst.elapsed < evalTime)
{
// Schrum2: Only called for non-visual evaluations
//Console.WriteLine("MAE:" + inst.elapsed + "/" + evalTime);
runEnvironment(env, inst, sem);
}
}
catch (Exception e)
{
for (int x = 0; x < inst.robots.Count; x++)
{
for (int y = 0; y < inst.robots[x].history.Count; y++)
{
Console.WriteLine(x + " " + y + " " + inst.robots[x].history[y].x + " " + inst.robots[x].history[y].y);
}
}
behavior = new SharpNeatLib.BehaviorType();
Console.WriteLine(e.Message);
Console.WriteLine(e.StackTrace);
throw (e);
}
double thisFit = inst.ff.calculate(this, env, inst, out behavior.objectives);
//Console.WriteLine(env.name + ": Fitness for one eval: " + thisFit);
fitnesses[evals] = thisFit;
tempFit += thisFit;
if (behavior != null && behavior.objectives != null && inst.bc != null)
{
for (int i = 0; i < behavior.objectives.Length; i++)
{
accumObjectives[i] += behavior.objectives[i];
}
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List <double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
inst.ff.reset();
}
// Schrum: Save each fitness separately
environmentScores[envNum] = tempFit;
envNum++; // go to the next environment
// Schrum: Product fitness might encourage better performance in all environments
if (multiplicativeFitness)
{
fitness *= tempFit / timesToRunEnvironments;
}
else // default is sum/average
{
fitness += tempFit / timesToRunEnvironments;
}
}
// Punish CPPN modules: Must use with multiobjective option
if (cppnModuleCost)
{
behavior.objectives = new double[] { fitness / environmentList.Count, -network.NumOutputModules };
}
// Punish CPPN links: Must use with multiobjective option
else if (cppnLinkCost)
{
behavior.objectives = new double[] { fitness / environmentList.Count, -network.NumLinks };
}
// Punish substrate links: Must use with multiobjective option
else if (substrateLinkCost)
{
behavior.objectives = new double[] { fitness / environmentList.Count, -linksInSubstrate };
}
else if (fitnessFunction is FourTasksFitness)
{ // Schrum: Special handling for FourTasksFitness ... could I just use accumObjectives?
behavior.objectives = environmentScores;
}
else // Original default
{
behavior.objectives = accumObjectives;
}
behavior.modules = network.NumOutputModules;
behavior.cppnLinks = network.NumLinks;
behavior.substrateLinks = linksInSubstrate;
//Console.WriteLine("Total: " + (fitness / environmentList.Count));
// Schrum: Averaging helps normalize range when adding fitness values, but not when multiplying fitness values
return(multiplicativeFitness ? fitness : fitness / environmentList.Count);
}
internal override double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
double fitness = 0;//SharpNeatLib.Utilities.random.NextDouble();
NeatGenome tempGenome;
INetwork controller;
behavior = new SharpNeatLib.BehaviorType();
double[] accumObjectives = new double[6];
for (int i = 0; i < 6; i++) accumObjectives[i] = 0.0;
IFitnessFunction fit_copy;
IBehaviorCharacterization bc_copy;
CollisionManager cm;
instance_pack inst = new instance_pack();
inst.timestep=timestep;
foreach (Environment env2 in environmentList)
{
fit_copy = fitnessFunction.copy();
if (behaviorCharacterization != null)
{
bc_copy = behaviorCharacterization.copy();
inst.bc = bc_copy;
}
inst.ff = fit_copy;
double tempFit = 0;
double[] fitnesses = new double[timesToRunEnvironments];
SharpNeatLib.Maths.FastRandom evalRand = new SharpNeatLib.Maths.FastRandom(100);
for (int evals = 0; evals < timesToRunEnvironments; evals++)
{
int agent_trials = timesToRunEnvironments;
inst.num_rbts=this.numberRobots;
Environment env = env2.copy();
double evalTime = evaluationTime;
inst.eval = evals;
env.seed = evals;
env.rng = new SharpNeatLib.Maths.FastRandom(env.seed + 1);
int noise_lev = (int)this.sensorNoise+1;
float new_sn = evalRand.NextUInt() % noise_lev;
float new_ef = evalRand.NextUInt() % noise_lev;
inst.agentBrain = new AgentBrain(homogeneousTeam, inst.num_rbts, substrateDescription, network, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate, dirComm);
initializeRobots(agentBrain, env, headingNoise, new_sn, new_ef, inst);
inst.elapsed = 0;
inst.timestep = this.timestep;
//Console.WriteLine(this.timestep);
inst.timeSteps = 0;
inst.collisionManager = collisionManager.copy();
inst.collisionManager.Initialize(env, this, inst.robots);
try {
while (inst.elapsed < evalTime)
{
runEnvironment(env,inst,sem);
}
} catch( Exception e) {
for(int x=0;x<inst.robots.Count;x++) {
for(int y=0;y<inst.robots[x].history.Count;y++) {
Console.WriteLine(x+" " + y+ " "+ inst.robots[x].history[y].x + " " + inst.robots[x].history[y].y);
}
}
behavior=new SharpNeatLib.BehaviorType();
Console.WriteLine(e.Message);
Console.WriteLine(e.StackTrace);
throw(e);
return 0.0001;
}
double thisFit = inst.ff.calculate(this, env, inst, out behavior.objectives);
fitnesses[evals] = thisFit;
tempFit += thisFit;
if (behavior != null && behavior.objectives != null && inst.bc!=null)
{
for (int i = 0; i < behavior.objectives.Length; i++)
accumObjectives[i] += behavior.objectives[i];
if(behavior.behaviorList==null)
behavior.behaviorList=new List<double>();
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
inst.ff.reset();
}
fitness += tempFit / timesToRunEnvironments;
}
behavior.objectives = accumObjectives;
return fitness / environmentList.Count;
}
public void reset()
{
seed = 0;
view_x = 0.0f;
view_y = 0.0f;
view_scale = 5.0f;
AOIRectangle = new Rectangle(30, 60, 640, 500);
group_orientation = 0;
robot_spacing = 30;
robot_heading = 0;
rng = new SharpNeatLib.Maths.FastRandom();
walls = new List<Wall>();
preys = new List<Prey>();
POIPosition = new List<Point>();
start_point = new Point2D(0, 0);
goal_point = new Point2D(100, 100);
}
/// <summary>
/// Runs a single individual (or single multiagent team) through the CurrentEnvironment(s) specified in the experiment file.
/// </summary>
internal override double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
double fitness = 0;
List <double> fitList = new List <double>();
behavior = new SharpNeatLib.BehaviorType();
List <Double> origBehavior = new List <Double>();
double[] accumObjectives = new double[6];
for (int i = 0; i < 6; i++)
{
accumObjectives[i] = 0.0;
}
IFitnessFunction fit_copy;
IBehaviorCharacterization bc_copy;
instance_pack inst = new instance_pack();
inst.timestep = timestep;
foreach (Environment env2 in environmentList)
{
fit_copy = fitnessFunction.copy();
if (behaviorCharacterization != null)
{
bc_copy = behaviorCharacterization.copy();
inst.bc = bc_copy;
}
inst.ff = fit_copy;
double tempFit = 0;
double[] fitnesses = new double[timesToRunEnvironments];
SharpNeatLib.Maths.FastRandom evalRand;
if (noiseDeterministic)
{
evalRand = new SharpNeatLib.Maths.FastRandom(100);
}
else
{
evalRand = new SharpNeatLib.Maths.FastRandom();
}
for (int evals = 0; evals < timesToRunEnvironments; evals++)
{
inst.num_rbts = this.numberRobots;
Environment env = env2.copy();
double evalTime = evaluationTime;
inst.eval = evals;
env.seed = evals;
if (!benchmark && noiseDeterministic)
{
env.rng = new SharpNeatLib.Maths.FastRandom(env.seed + 1);
}
else
{
env.rng = new SharpNeatLib.Maths.FastRandom();
}
float new_sn = this.sensorNoise;
float new_ef = this.effectorNoise;
float new_headingnoise = this.headingNoise;
inst.agentBrain = new AgentBrain(homogeneousTeam, inst.num_rbts, substrateDescription, network, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate, neatBrain, useCTRNNS);
inst.timestep = timestep;
initializeRobots(agentBrain, env, new_headingnoise, new_sn, new_ef, inst);
inst.elapsed = 0;
inst.timeSteps = 0;
inst.collisionManager = collisionManager.copy();
inst.collisionManager.initialize(env, this, inst.robots);
while (inst.elapsed < evalTime)
{
runEnvironment(env, inst, sem);
}
double thisFit = inst.ff.calculate(this, env, inst, out behavior.objectives);
fitnesses[evals] = thisFit;
tempFit += thisFit;
if (behavior != null && behavior.objectives != null && inst.bc != null)
{
for (int i = 0; i < behavior.objectives.Length; i++)
{
accumObjectives[i] += behavior.objectives[i];
}
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List <double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
else if (behavior != null && inst.bc != null)
{
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List <double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
inst.ff.reset();
}
fitness += tempFit / timesToRunEnvironments;
fitList.Add(tempFit / timesToRunEnvironments);
}
behavior.objectives = accumObjectives;
if (behaviorCharacterization != null)
{
behavior.wraparoundRange = behaviorCharacterization.wraparoundDistCalc;
}
double t = 0;
if (recordEndpoints)
{
behavior.finalLocation = new List <double>(2);
behavior.finalLocation.Add(inst.robots[0].Location.X);
behavior.finalLocation.Add(inst.robots[0].Location.Y);
}
if (recordTrajectories)
{
behavior.trajectory = new List <int>();
behavior.trajectory.AddRange(inst.robots[0].Trajectory);
}
return((fitness - t) / environmentList.Count);
}
internal override double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
double fitness = 0;//SharpNeatLib.Utilities.random.NextDouble();
NeatGenome tempGenome;
INetwork controller;
behavior = new SharpNeatLib.BehaviorType();
double[] accumObjectives = new double[6];
for (int i = 0; i < 6; i++)
{
accumObjectives[i] = 0.0;
}
IFitnessFunction fit_copy;
IBehaviorCharacterization bc_copy;
CollisionManager cm;
instance_pack inst = new instance_pack();
inst.timestep = timestep;
foreach (Environment env2 in environmentList)
{
fit_copy = fitnessFunction.copy();
if (behaviorCharacterization != null)
{
bc_copy = behaviorCharacterization.copy();
inst.bc = bc_copy;
}
inst.ff = fit_copy;
double tempFit = 0;
double[] fitnesses = new double[timesToRunEnvironments];
SharpNeatLib.Maths.FastRandom evalRand = new SharpNeatLib.Maths.FastRandom(100);
for (int evals = 0; evals < timesToRunEnvironments; evals++)
{
int agent_trials = timesToRunEnvironments;
inst.num_rbts = this.numberRobots;
Environment env = env2.copy();
double evalTime = evaluationTime;
inst.eval = evals;
env.seed = evals;
env.rng = new SharpNeatLib.Maths.FastRandom(env.seed + 1);
int noise_lev = (int)this.sensorNoise + 1;
float new_sn = evalRand.NextUInt() % noise_lev;
float new_ef = evalRand.NextUInt() % noise_lev;
inst.agentBrain = new AgentBrain(homogeneousTeam, inst.num_rbts, substrateDescription, network, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate);
initializeRobots(agentBrain, env, headingNoise, new_sn, new_ef, inst);
inst.elapsed = 0;
inst.timestep = this.timestep;
//Console.WriteLine(this.timestep);
inst.timeSteps = 0;
inst.collisionManager = collisionManager.copy();
inst.collisionManager.Initialize(env, this, inst.robots);
try
{
while (inst.elapsed < evalTime)
{
runEnvironment(env, inst, sem);
}
}
catch (Exception e)
{
for (int x = 0; x < inst.robots.Count; x++)
{
for (int y = 0; y < inst.robots[x].history.Count; y++)
{
Console.WriteLine(x + " " + y + " " + inst.robots[x].history[y].x + " " + inst.robots[x].history[y].y);
}
}
behavior = new SharpNeatLib.BehaviorType();
Console.WriteLine(e.Message);
Console.WriteLine(e.StackTrace);
throw (e);
return(0.0001);
}
double thisFit = inst.ff.calculate(this, env, inst, out behavior.objectives);
fitnesses[evals] = thisFit;
tempFit += thisFit;
if (behavior != null && behavior.objectives != null && inst.bc != null)
{
for (int i = 0; i < behavior.objectives.Length; i++)
{
accumObjectives[i] += behavior.objectives[i];
}
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List <double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
inst.ff.reset();
}
fitness += tempFit / timesToRunEnvironments;
}
behavior.objectives = accumObjectives;
return(fitness / environmentList.Count);
}
/// <summary>
/// Initialize the robot.
/// </summary>
public void init(int id, double locationX, double locationY, double heading, AgentBrain agentBrain, Environment environment, float sensorNoise, float effectorNoise, float headingNoise, float timeStep)
{
this.ID = id;
Radius = defaultRobotSize();
Location = new Point2D(locationX, locationY);
AreaOfImpact = new Circle2D(Location, Radius);
OldLocation = new Point2D(Location);
Heading = heading;
Velocity = 0.0;
HasCollided = false;
this.Timestep = timeStep;
this.CurrentEnvironment = environment;
this.Brain = agentBrain;
this.SensorNoise = sensorNoise;
this.EffectorNoise = effectorNoise;
this.HeadingNoise = headingNoise;
populateSensors();
if (environment.seed != -1)
rng = environment.rng;
else
rng = environment.rng;
this.Trajectory = new List<int>();
}
public void init(int id, double nx, double ny, double dir,
AgentBrain agentBrain, Environment environment,
float sensorNoise, float effectorNoise, float headingNoise, float timeStep)
{
steps = 0;
this.id = id;
radius = defaultRobotSize();
location = new Point2D(nx, ny);
circle = new Circle2D(location, radius);
old_location = new Point2D(location);
heading = dir;
velocity = 0.0;
collide_last = false;
this.timeStep = timeStep;
this.environment = environment;
this.agentBrain = agentBrain;
this.sensorNoise = sensorNoise;
this.effectorNoise = effectorNoise;
this.headingNoise = headingNoise;
populateSensors();
if(environment.seed != -1)
rng=environment.rng; //new SharpNeatLib.Maths.FastRandom(environment.seed); //Utilities.random;
else
rng=environment.rng; //new SharpNeatLib.Maths.FastRandom();
}
public FastRandom(int seed) : base(seed)
{
_random = new SharpNeatLib.Maths.FastRandom(seed);
}
/// <summary>
/// Runs a single individual (or single multiagent team) through the CurrentEnvironment(s) specified in the experiment file.
/// </summary>
internal override double evaluateNetwork(INetwork network, out SharpNeatLib.BehaviorType behavior, System.Threading.Semaphore sem)
{
double fitness = 0;
List<double> fitList = new List<double>();
behavior = new SharpNeatLib.BehaviorType();
List<Double> origBehavior = new List<Double>();
double[] accumObjectives = new double[6];
for (int i = 0; i < 6; i++) accumObjectives[i] = 0.0;
IFitnessFunction fit_copy;
IBehaviorCharacterization bc_copy;
instance_pack inst = new instance_pack();
inst.timestep = timestep;
foreach (Environment env2 in environmentList)
{
fit_copy = fitnessFunction.copy();
if (behaviorCharacterization != null)
{
bc_copy = behaviorCharacterization.copy();
inst.bc = bc_copy;
}
inst.ff = fit_copy;
double tempFit = 0;
double[] fitnesses = new double[timesToRunEnvironments];
SharpNeatLib.Maths.FastRandom evalRand;
if (noiseDeterministic) evalRand = new SharpNeatLib.Maths.FastRandom(100);
else
{
evalRand = new SharpNeatLib.Maths.FastRandom();
}
for (int evals = 0; evals < timesToRunEnvironments; evals++)
{
inst.num_rbts = this.numberRobots;
Environment env = env2.copy();
double evalTime = evaluationTime;
inst.eval = evals;
env.seed = evals;
if (!benchmark && noiseDeterministic)
env.rng = new SharpNeatLib.Maths.FastRandom(env.seed + 1);
else
{
env.rng = new SharpNeatLib.Maths.FastRandom();
}
float new_sn = this.sensorNoise;
float new_ef = this.effectorNoise;
float new_headingnoise = this.headingNoise;
inst.agentBrain = new AgentBrain(homogeneousTeam, inst.num_rbts, substrateDescription, network, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate, neatBrain, useCTRNNS);
inst.timestep = timestep;
initializeRobots(agentBrain, env, new_headingnoise, new_sn, new_ef, inst);
inst.elapsed = 0;
inst.timeSteps = 0;
inst.collisionManager = collisionManager.copy();
inst.collisionManager.initialize(env, this, inst.robots);
while (inst.elapsed < evalTime)
{
runEnvironment(env, inst, sem);
}
double thisFit = inst.ff.calculate(this, env, inst, out behavior.objectives);
fitnesses[evals] = thisFit;
tempFit += thisFit;
if (behavior != null && behavior.objectives != null && inst.bc != null)
{
for (int i = 0; i < behavior.objectives.Length; i++)
accumObjectives[i] += behavior.objectives[i];
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List<double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
else if (behavior != null && inst.bc != null)
{
if (behavior.behaviorList == null)
{
behavior.behaviorList = new List<double>();
}
behavior.behaviorList.AddRange(inst.bc.calculate(this, inst));
inst.bc.reset();
}
inst.ff.reset();
}
fitness += tempFit / timesToRunEnvironments;
fitList.Add(tempFit / timesToRunEnvironments);
}
behavior.objectives = accumObjectives;
if (behaviorCharacterization != null)
{
behavior.wraparoundRange = behaviorCharacterization.wraparoundDistCalc;
}
double t = 0;
if (recordEndpoints)
{
behavior.finalLocation = new List<double>(2);
behavior.finalLocation.Add(inst.robots[0].Location.X);
behavior.finalLocation.Add(inst.robots[0].Location.Y);
}
if (recordTrajectories)
{
behavior.trajectory = new List<int>();
behavior.trajectory.AddRange(inst.robots[0].Trajectory);
}
return (fitness - t) / environmentList.Count;
}
