override public int Search()
{
CtwContextTreeUndo undoInstance = new CtwContextTreeUndo(this);
MonteCarloSearchNode searchTree = new MonteCarloSearchNode(MonteCarloSearchNode.DecisionNode);
for (int i = 0; i < this.McSimulations; i++)
{
searchTree.Sample(this, this.Horizon);
this.model_revert(undoInstance);
}
//searchTree.PrintBs();
int bestAction = -1;
double bestMean = double.NegativeInfinity;
foreach (int action in this.Environment.ValidActions)
{
if (!searchTree.Children.ContainsKey(action))
{
continue;
}
double mean = searchTree.Children[action].Mean + Utils.RandomDouble(0, 0.0001);
if (mean > bestMean)
{
bestMean = mean;
bestAction = action;
}
}
return(bestAction);
}
public int SelectAction(Agent agent)
{
Debug.Assert(agent.MaximumReward() != null, "this is weird place, - in selection action");
double exploreBias = (double)agent.Horizon * agent.MaximumReward().Value;
double explorationNumerator = this.ExplorationConstant * Math.Log(this.Visits);
int bestAction = -1;
double bestPriority = double.NegativeInfinity;
foreach (int action in agent.Environment.ValidActions)
{
MonteCarloSearchNode node = null;
if (this.Children.ContainsKey(action))
{
node = this.Children[action];
}
double priority;
if (node == null || node.Visits == 0)
{
// previously unexplored node
priority = this.UnexploredBias; //unexplored bias
}
else
{
priority = node.Mean + exploreBias * Math.Sqrt(explorationNumerator / node.Visits);
}
if (priority > (bestPriority + Utils.RandomDouble(0, 0.001)))
{
bestAction = action;
bestPriority = priority;
}
}
return(bestAction);
}
public double Sample(Agent agent, int horizon)
{
double reward = 0.0;
if (horizon == 0)
{
return((int)reward);
}
else if (this.Type == ChanceNode)
{
var percept = agent.GeneratePerceptAndUpdate();
int observation = percept.Item1;
int randomReward = percept.Item2;
if (!this.Children.ContainsKey(observation)) //new node ->add it as decision node
{
this.Children[observation] = new MonteCarloSearchNode(DecisionNode);
}
MonteCarloSearchNode observationChild = this.Children[observation];
reward = randomReward + observationChild.Sample(agent, horizon - 1);
}
else if (this.Visits == 0) //unvisited decision node or we have exceeded maximum tree depth
{
reward = agent.Playout(horizon);
// Console.WriteLine("from playout: reward ="+reward);
}
else //Previously visited decision node
{
int actionNullable = this.SelectAction(agent);
int action = actionNullable;
agent.ModelUpdateAction(action);
if (!this.Children.ContainsKey(action)) //this action is new chance child
{
this.Children[action] = new MonteCarloSearchNode(ChanceNode);
}
MonteCarloSearchNode actionChild = this.Children[action];
reward = actionChild.Sample(agent, horizon); //it is not clear if not horizon-1. (asks pyaixi)
}
double visitsDouble = this.Visits;
//Console.WriteLine("> {3} - {0}, {1}, {2}", this.mean, reward, (reward + (visitsDouble * this.mean) / (visitsDouble + 1.0)), visitsDouble);
this.Mean = (reward + (visitsDouble * this.Mean)) / (1.0 + visitsDouble);
this.Visits = this.Visits + 1;
return(reward);
}
public override int Search()
{
CtwContextTreeUndo undoInstance = new CtwContextTreeUndo(this);
MonteCarloSearchNode searchTree = new MonteCarloSearchNode(MonteCarloSearchNode.DecisionNode);
for (int i = 0; i < this.McSimulations; i++) {
searchTree.Sample(this, this.Horizon);
this.model_revert(undoInstance);
}
//searchTree.PrintBs();
int bestAction=-1;
double bestMean = double.NegativeInfinity;
foreach (int action in this.Environment.ValidActions) {
if (!searchTree.Children.ContainsKey(action)) {
continue;
}
double mean = searchTree.Children[action].Mean + Utils.RandomDouble(0, 0.0001);
if (mean > bestMean) {
bestMean = mean;
bestAction = action;
}
}
return bestAction;
}