protected override Result run (BehaviourTree.DataContext dataContext)
{
if (this._person.currentBlock == null || !QLearningCache.Instance.IsCacheAvailable(ImprovedNPC.GUEST_QLEARNING))
{
return Node.Result.FAILED;
}
Vector3 lhs = this._person.transform.forward;
if (this._person.previousBlock != null)
{
lhs = this._person.currentBlock.transform.position - this._person.previousBlock.transform.position;
lhs.y = 0f;
lhs.Normalize();
}
BlockNeighbour[] connected = this._person.currentBlock.getConnected();
WeightedPick<Block> cantWander = new WeightedPick<Block> ();
WeightedPick<Block> canWander = new WeightedPick<Block> ();
WeightedPick<Block> interestingBlocks = new WeightedPick<Block> ();
bool bounce = true;
Block block = null;
float reward = 0.0f;
for (int i = 0; i < connected.Length; i++)
{
BlockNeighbour blockNeighbour = connected[i];
Block connect_blocks = blockNeighbour.block;
bool canWanderOnto = this._person.canWanderOnto (connect_blocks);
Vector3 rhs = connect_blocks.transform.position - this._person.currentBlock.transform.position;
rhs.y = 0f;
rhs.Normalize();
if (Vector3.Dot(lhs, rhs) < -0.5f)
{
block = connect_blocks;
reward = Config.BOUNCE_COST;
}
else if (connect_blocks is Path && this.canStepOntoPathWithoutThinking((Path)connect_blocks))
{
if (canWanderOnto)
{
var current = QLearningCache.Instance.GetNode (ImprovedNPC.GUEST_QLEARNING, Mathf.FloorToInt (_person.transform.position.x), Mathf.RoundToInt (_person.transform.position.y), Mathf.FloorToInt (_person.transform.position.z),_person.currentBlock,_person);
var future = QLearningCache.Instance.GetNode (ImprovedNPC.GUEST_QLEARNING, (int)connect_blocks.intPosition.x,(int)connect_blocks.intPosition.y, (int)connect_blocks.intPosition.z,connect_blocks,_person);
float potentialReward = current.findMaxUtility (future);
canWander.Add (potentialReward , connect_blocks,Config.WANDERING_NEGATIVE_WEIGHT_FACTOR,Config.WANDERING_POSITIVE_WEIGHT_FACTOR);
}
else
{
var current = QLearningCache.Instance.GetNode (ImprovedNPC.GUEST_QLEARNING, Mathf.FloorToInt (_person.transform.position.x), Mathf.RoundToInt (_person.transform.position.y), Mathf.FloorToInt (_person.transform.position.z),_person.currentBlock,_person);
var future = QLearningCache.Instance.GetNode (ImprovedNPC.GUEST_QLEARNING, (int)connect_blocks.intPosition.x,(int)connect_blocks.intPosition.y, (int)connect_blocks.intPosition.z,connect_blocks,_person);
float potentialReward = current.findMaxUtility (future);
cantWander.Add (potentialReward, connect_blocks,Config.WANDERING_NEGATIVE_WEIGHT_FACTOR,Config.WANDERING_POSITIVE_WEIGHT_FACTOR);
}
}
else
{
float max_interest = 0f;
PersonBehaviour[] components = this._person.GetComponents<PersonBehaviour>();
for (int j = 0; j < components.Length; j++)
{
PersonBehaviour personBehaviour = components[j];
if (personBehaviour is WalkToInterestCalculator)
{
float interest = (personBehaviour as WalkToInterestCalculator).calculateInterest(connect_blocks, this._person);
if (interest > max_interest)
{
max_interest = interest;
}
}
}
if (max_interest > 0f && !(connect_blocks is Exit))
{
interestingBlocks.Add (max_interest, connect_blocks,Config.FINDING_ATTRACTION_NEGATIVE_WEIGHT_FACTOR,Config.FINDING_ATTRACTION_POSITIVE_WEIGHT_FACTOR);
}
}
}
if (interestingBlocks.NumberOfPairs () >= 1) {
var weightPair = interestingBlocks.RandomPick ();
block = weightPair.Item;
reward = weightPair.Weight;
bounce = false;
} else if (canWander.NumberOfPairs () >= 1) {
block = canWander.RandomPick ().Item;
reward = 0.0f;
bounce = false;
} else if(cantWander.NumberOfPairs() >= 1){
block = cantWander.RandomPick ().Item;
reward = 0.0f;
bounce = false;
}
if (block == null) {
if (this._person.currentBlock != null) {
block = _person.currentBlock;
} else {
//when the guest turns around
block = _person.previousBlock;
reward = Config.BOUNCE_COST;
}
}
dataContext.set (this._bounce, bounce);
dataContext.set (this._reward, reward);
dataContext.set(this._outblock, block);
return Node.Result.SUCCESS;
}