public int numBrains; // Schrum: Total number
public AgentBrain(bool homogenous, int numAgents, SubstrateDescription substrateDescription, INetwork genome,
bool normalizeANNWeights, bool adaptableANN, bool modulatoryANN, bool multi, int brains, bool evolveSubstrate, bool preferenceNeurons, bool forcedSituationalPolicyGeometry)
{
this.evolveSubstrate = evolveSubstrate;
this.normalizeANNWeights = normalizeANNWeights;
this.adaptableANN = adaptableANN;
this.modulatoryANN = modulatoryANN;
this.genome = genome;
this.substrateDescription = substrateDescription;
this.numRobots = numAgents;
this.homogenous = homogenous;
this.multipleBrains = multi;
this.forcedSituationalPolicyGeometry = forcedSituationalPolicyGeometry;
// Schrum: When preference neurons are used, the number of modules in the network is the
// more reliable source of information. Especially if Module Mutation will allow more modules
// to be created, each creating a new brain.
this.numBrains = genome != null && preferenceNeurons ? genome.NumOutputModules : brains;
this.preferenceNeurons = preferenceNeurons;
//inputCounter = 0;
teamInput = new float[numAgents * substrateDescription.InputCount];
activated = new bool[numAgents];
createBrains();
robotListeners = new List<Robot>();
}
/// <summary>
/// Constructs a new AgentBrain object from the specified parameters. This brain is designed to be used as a multiagent hivemind, but can also be used to control a single agent by setting numAgents equal to 1.
/// </summary>
/// <param name="homogeneous">Is the team homogeneous? Agents in a homogeneous team all have identical copies of the same brain, while those in a heterogeneous team do not, instead each contributing to one central, distributed hive brain.</param>
/// <param name="numAgents">Set to 1 for an individual agent, or >1 for a multiagent team.</param>
/// <param name="substrateDescription">HyperNEAT substrate</param>
/// <param name="genome">If using NEAT, this should be the already-decoded neural network. Otherwise, if using HyperNEAT, this should be the CPPN that encodes the neural network.</param>
/// <param name="normalizeANNWeights"></param>
/// <param name="adaptableANN"></param>
/// <param name="modulatoryANN"></param>
/// <param name="multi">Multiple brains with situational policies?</param>
/// <param name="evolveSubstrate">Set to true to enable ES-HyperNEAT.</param>
/// <param name="useNeatBrain">If false, the system will use HyperNEAT as the EA.</param>
/// <param name="useCTRNNs">Set to true to use continuous time recurrent neural networks.</param>
public AgentBrain(bool homogeneous, int numAgents, SubstrateDescription substrateDescription, INetwork genome,
bool normalizeANNWeights, bool adaptableANN, bool modulatoryANN, bool multi, bool evolveSubstrate, bool useNeatBrain, bool useCTRNNs = false)
{
// Set instance variables
EvolveSubstrate = evolveSubstrate;
NormalizeANNWeights = normalizeANNWeights;
AdaptableANN = adaptableANN;
ModulatoryANN = modulatoryANN;
Genome = genome;
SubstrateDescription = substrateDescription;
NumRobots = numAgents;
Homogeneous = homogeneous;
MultipleBrains = multi;
NeatBrain = useNeatBrain;
UseCTRNNs = useCTRNNs;
// Initialiaze team arrays
TeamInput = new float[numAgents * substrateDescription.InputCount];
TeamInputOld = new float[numAgents * substrateDescription.InputCount];
TeamOutput = new float[numAgents * substrateDescription.OutputCount];
TeamOutputOld = new float[numAgents * substrateDescription.OutputCount];
TeamHidden = new float[numAgents * 5];
TeamHiddenOld = new float[numAgents * 5];
RobotInput = new float[substrateDescription.InputCount];
RobotInputOld = new float[substrateDescription.InputCount];
Activated = new bool[numAgents];
// Initialize the agents' brains
createBrains();
// Register listeners for the robots' neural network outputs
RobotListeners = new List<Robot>();
if (OutputComms)
{
// If the robots' input and output streams have already been initialized, reset them
if (OutputsInitialized)
{
for (int j = 0; j < numAgents; j++)
{
OutStreams[j].Close();
InStreams[j].Close();
OutStreams[j] = null;
InStreams[j] = null;
}
}
// Initialize the robots' input and output streams
OutStreams = new List<StreamWriter>(numAgents);
InStreams = new List<StreamWriter>(numAgents);
for (int j = 0; j < numAgents; j++)
{
OutStreams.Add(new StreamWriter("agentO" + j + ".txt"));
InStreams.Add(new StreamWriter("agentI" + j + ".txt"));
}
OutputsInitialized = true;
}
}
public AgentBrain(bool homogenous, int numAgents, SubstrateDescription substrateDescription, INetwork genome,
bool normalizeANNWeights, bool adaptableANN, bool modulatoryANN, bool multi, bool evolveSubstrate, bool dirComm)
{
this.evolveSubstrate = evolveSubstrate;
this.normalizeANNWeights = normalizeANNWeights;
this.adaptableANN = adaptableANN;
this.modulatoryANN = modulatoryANN;
this.genome = genome;
this.substrateDescription = substrateDescription;
this.numRobots = numAgents;
this.homogenous = homogenous;
this.multipleBrains = multi;
this.dirComm = dirComm;
//inputCounter = 0;
teamInput = new float[numAgents * substrateDescription.InputCount];
activated = new bool[numAgents];
createBrains();
robotListeners = new List<Robot>();
}
protected void setupVariables()
{
robots = new List<Robot>();
substrateDescription = new SubstrateDescription(substrateDescriptionFilename);
agentBrain = new AgentBrain(homogeneousTeam, numberRobots, substrateDescription, genome != null ? genome.Decode(null) : null, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate, dirComm);
//if(homogeneousTeam)
//Console.WriteLine("inc:"+agentBrain.getBrain(0).InputNeuronCount);
loadEnvironments(this);
//agentsCollide=environment.agentsCollide;
//agentsVisible=environment.agentsVisible;
//Substrate sensor density
initializeRobots(agentBrain, environment, headingNoise, sensorNoise, effectorNoise, null);
setFitnessFunction(fitnessFunctionName);
setBehavioralCharacterization(behaviorCharacterizationName);
if (gridCollision)
collisionManager = new GridCollision();
else
collisionManager = new StandardCollision();
collisionManager.Initialize(environment, this, this.robots);
timeSteps = 0;
elapsed = 0;
if (initialized)
foreach (Prey p in environment.preys)
{
p.reset();
}
preyCaught = 0;
}
/// <summary>
/// Performs the actual initialization logic.
/// </summary>
protected void setupVariables()
{
substrateDescription = new SubstrateDescription(substrateDescriptionFilename);
agentBrain = new AgentBrain(homogeneousTeam, numberRobots, substrateDescription, genome != null ? genome.Decode(null) : null, normalizeWeights, adaptableANN, modulatoryANN, multibrain, evolveSubstrate, neatBrain, useCTRNNS);
loadEnvironments(this);
initializeRobots(agentBrain, environment, headingNoise, sensorNoise, effectorNoise, null);
setFitnessFunction(fitnessFunctionName);
setBehavioralCharacterization(behaviorCharacterizationName);
collisionManager = new StandardCollision();
collisionManager.initialize(environment, this, this.robots);
timeSteps = 0;
elapsedTime = 0;
}
