void AddPlayers(bool challenge)
{
raceManager.ResetPlayers();
if (challenge)
{
KeyCode[] keycodes = new KeyCode[4];
keycodes[0] = KeyCode.UpArrow;
if (!GA_Parameters.breakWithSpace)
{
keycodes[1] = KeyCode.DownArrow;
}
else
{
keycodes[1] = KeyCode.Space;
}
keycodes[2] = KeyCode.LeftArrow;
keycodes[3] = KeyCode.RightArrow;
raceManager.AddHumanPlayer("Me", keycodes);
}
for (int i = 0; i < activeNetworks.Count; i++)
{
raceManager.AddAIPlayer(i.ToString(), activeNetworks[i].CreateNetwork(), i);
}
raceManager.FinishPlayers(activeNetworks.Count);
}
// train the neural networks environment
IEnumerator Train()
{
bool complexify = true;
int index = 0;
// Create the initial neural networks
generationNetworks = ga.CreateNetworks();
raceManager.Reset();
FitnessTracker.totLength = 0;
for (int i = 0; i < LoadTrackManager.instance.selectedTrackNames.Count; i++)
{
trackManagers.Add(Instantiate(trackPrefab).GetComponent <TrackManager>());
trackManagers[i].Initialize(LoadTrackManager.instance.selectedTrackNames[i]);
trackManagers[i].gameObject.SetActive(false);
}
TrackManager.SetTotalLength();
// Training loop. Keep the training running as long as desired.
while (true)
{
// This pauses the training
yield return(StartCoroutine(Pause()));
fitnesses.Clear();
float sum = 0;
// add all new networks to the networks list
raceManager.ResetPlayers();
for (int individual = 0; individual < generationNetworks.Count; individual++)
{
raceManager.AddAIPlayer(individual.ToString(), generationNetworks[individual], individual);
}
raceManager.FinishPlayers(generationNetworks.Count);
yield return(StartCoroutine(raceManager.StartRace(true, trackManagers, false)));
// Get the fitnesses of the networks and calculate the sum of all fitnesses
fitnesses = raceManager.GetFitnesses();
int bestRacerIndex = 0;
bool updateTime = false;
for (int i = 0; i < fitnesses.Count; i++)
{
if (fitnesses[i] >= bestFitnessSoFar)
{
bestFitnessSoFar = fitnesses[i];
bestRacerIndex = i;
updateTime = true;
bestGeneration = curGeneration;
}
if (fitnesses[i] < 0)
{
fitnesses[i] = 0;
}
sum += fitnesses[i];
}
float currentBestFitness = 0;
for (int i = 0; i < fitnesses.Count; i++)
{
if (fitnesses[i] >= currentBestFitness)
{
currentBestFitness = fitnesses[i];
}
}
curGeneration++;
// Restart the simulation if non of the cars has a fitness higher than 0
if (sum == 0)
{
curGeneration = 0;
InitializeNetworks();
generationNetworks = ga.CreateNetworks();
continue;
}
float totalTime = raceManager.GetCurrentCompetingCars()[bestRacerIndex].totalTime;
if (raceManager.GetCurrentCompetingCars()[bestRacerIndex].GetFitnessTracker().finished != LoadTrackManager.instance.selectedTrackNames.Count)
{
totalTime = -1;
}
// Update the best and average fitness on the UI
UIController.instance.UpdateUI(bestFitnessSoFar, currentBestFitness, sum / ga.populationSize, curGeneration, totalTime, updateTime);
// Determine the population for the next generation based on the fitnesses of the current networks
generationNetworks = ga.DoGeneration(fitnesses, complexify);
index++;
if (index > 50)
{
index = 0;
if (complexify && Random.Range(0f, 1f) < 0.3f)
{
complexify = !complexify;
}
else if (!complexify)
{
complexify = true;
}
}
}
}