//Gives you the max Q-Value possible from a determined state (cell).
private double CalculateEstimate(int nextCell)
{
double estimate = 0;
if (!LabryntRules.IsFinalState(nextCell))
{
Movement bestMove = GetBestMovement(nextCell);
estimate = qMat.GetQValue(nextCell, bestMove);
}
return(estimate);
}
// Update is called once per frame
void Update()
{
if (!paused)
{
timer += Time.deltaTime;
float dt = speedSelector.GetInverse();
if (timer > dt)
{
//Calculate how many choices we need according to the speed selector.
int loops = (int)(timer / dt);
while (loops > 0)
{
if (restart)
{
InitialiseAlgorithm();
restart = false;
newEpisode = true;
}
if (newEpisode)
{
SetValuesForNewEpisode();
newEpisode = false;
}
Movement currentMove;
//Choose action (a) based on policy (p)
if (ShouldExplore())
{
currentMove = GetRandomMovement(currentCell);
}
else
{
currentMove = GetBestMovement(currentCell);
}
//Observe reward (r)
int reward = LabryntRules.GetReward(currentCell, currentMove);
double oldQValue = qMat.GetQValue(currentCell, currentMove);
//Do an estimation of the best Q-value to obtain from next state.
int nextCell = LabryntRules.GetLandingCell(currentCell, currentMove);
double estimation = CalculateEstimate(nextCell);
//Recalculate Q-value for the current cell based on estimation.
double updatedQValue = CalculateNewQValue(oldQValue, alpha, gamma, reward, estimation);
qMat.SetQValue(currentCell, currentMove, updatedQValue);
cumulativeReward += reward;
currentCell = nextCell;
UpdatePlayerPositionInDisplay(); //Now we update the display with the new player position.
//If the player landed in an final state cell, we need to update epsilon and start a new episode.
if (LabryntRules.IsFinalState(currentCell))
{
//Change epsilon parameter so it shifts from exploration to explotation gradually between episodes.
if (epsilon > 0.3)
{
epsilon *= epsilonDecay1;
}
if (epsilon < 0.3)
{
epsilon *= epsilonDecay2;
}
newEpisode = true;
}
loops--;
}
timer -= dt;
}
}
}
