public int currentTrialForEdit = 0; // keeps track of which trial index is currently being edited while in Fitness/Mini-game Panels
// For Each Trial:
// MiniGame Class to store gametype and settings
// Fitness Function Class w/ Fitness component list and settings
// Constructor Functions:
public Player() {
DebugBot.DebugFunctionCall("Player; Player() Constructor!; ", debugFunctionCalls);
InitializeNewPopulation();
masterTrialsList = new List<Trial>();
Trial newTrial = new Trial(this); // make trials list one member long, with a None mini-game value
masterTrialsList.Add(newTrial);
masterCupid = new CrossoverManager();
dataManager = new DataManager(this);
graphKing = new TheGraphKing();
//newTrial.playerRef = this;
}
public void InitializeLoadedPopulation()
{
// Assumes template has been set from defaults!
ResetRepresentativesList();
fitnessManager.InitializeLoadedData(agentGenomeList.Count);
graphKing = new TheGraphKing();
// ENV:
//ResetRepresentativesList();
// Fitness Manager
//fitnessManager.InitializeLoadedData(popSize);
// Training Settings Manager:
// -- so simple at this point no init is needed, it's just 2 floats
}
public int currentTrialForEdit = 0; // keeps track of which trial index is currently being edited while in Fitness/Mini-game Panels
// For Each Trial:
// MiniGame Class to store gametype and settings
// Fitness Function Class w/ Fitness component list and settings
// Constructor Functions:
public Player()
{
DebugBot.DebugFunctionCall("Player; Player() Constructor!; ", debugFunctionCalls);
InitializeNewPopulation();
masterTrialsList = new List <Trial>();
Trial newTrial = new Trial(this); // make trials list one member long, with a None mini-game value
masterTrialsList.Add(newTrial);
masterCupid = new CrossoverManager();
dataManager = new DataManager(this);
graphKing = new TheGraphKing();
//newTrial.playerRef = this;
}
// Representative system will be expanded later - for now, just defaults to Top # of performers
public PlayerPopulation(Challenge.Type challengeType, BodyGenome bodyTemplate, int numGenomes, int numBaseline, int numReps)
{
bodyGenomeTemplate = new BodyGenome();
bodyGenomeTemplate.CopyBodyGenomeFromTemplate(bodyTemplate);
graphKing = new TheGraphKing();
popSize = numGenomes;
this.numBaseline = numBaseline;
// Create blank AgentGenomes for the standard population
agentGenomeList = new List <AgentGenome>();
historicGenomePool = new List <AgentGenome>();
baselineGenomePool = new List <AgentGenome>();
for (int j = 0; j < numGenomes; j++)
{
AgentGenome agentGenome = new AgentGenome(j);
agentGenome.InitializeBodyGenomeFromTemplate(bodyGenomeTemplate);
agentGenome.InitializeRandomBrainFromCurrentBody(0.0f);
agentGenomeList.Add(agentGenome);
}
RepopulateBaselineGenomes();
AppendBaselineGenomes();
// Representatives:
numPerformanceReps = numReps;
ResetRepresentativesList();
historicGenomePool.Add(agentGenomeList[0]); // init
fitnessManager = new FitnessManager();
SetUpDefaultFitnessComponents(challengeType, fitnessManager);
fitnessManager.ResetHistoricalData();
fitnessManager.ResetCurrentHistoricalDataLists();
fitnessManager.InitializeForNewGeneration(agentGenomeList.Count);
trainingSettingsManager = new TrainingSettingsManager(0.01f, 0.8f, 0.2f, 0.005f);
}
