public override QState Run(QState currentState, int trialNum, decimal learn, decimal discount, decimal explore)
{
QSearch qsearch = new QSearch(this);
QSearchResult actions = qsearch.Depth_First(currentState, true);
if (actions != null)
{
foreach (QAction action in actions.actionsList)
{
if (!currentState.IsEnd() && isRunning && currentState.GetActions().Contains(action))
{
WriteOutput(currentState + ": " + action);
QState newState = currentState.GetNewState(action);
newState.Inherit(currentState);
newState.Step();
currentState = newState;
}
}
if (currentState.IsEnd())
{
WriteOutput(currentState + ": End");
}
else
{
WriteOutput("Existing solution no longer applicable. Re-solving...");
return(Run(currentState, trialNum, learn, discount, explore));
}
}
else
{
WriteOutput("No solution found.", true);
}
return(currentState);
}
// This runs a single trial/instance of the QState problem. QLearner will automatically run many times for learning or once to apply what has been learned.
// Must return the final state
public override QState Run(QState currentState, int trialNum, decimal learn, decimal discount, decimal explore)
{
while (!currentState.IsEnd() && currentState.GetActions().Length > 0 && isRunning)
{
QAction action = currentState.GetActions().ElementAt(r.Next(currentState.GetActions().Length));
QState newState = currentState.GetNewState(action);
newState.Inherit(currentState);
newState.Step();
WriteOutput((CurrentMode == LEARN ? "Trial " + trialNum + ", " : "") + ": '" + action + "' @ " + currentState.ToString());
currentState = newState;
}
return(currentState);
}
public override QState Run(QState currentState, int trialNum, decimal learn, decimal discount, decimal explore)
{
this.learn = learn; this.discount = discount; this.explore = explore;
decimal score = 0;
int actionsTaken = 0;
while (!currentState.IsEnd() && GetOutcomes(currentState).Count > 0 && isRunning)
{
actionsTaken++;
QAction a;
bool exp;
if (explore > 0 && (decimal)random.NextDouble() <= explore)
{
a = GetRandomAction(currentState);
exp = true;
}
else
{
a = GetBestAction(currentState);
exp = false;
}
QState newState = currentState.GetNewState(a);
WriteOutput((CurrentMode == LEARN ? "Trial " + trialNum + ", " : "") + "#" + actionsTaken + " " + (exp ? "Explore" : "Action") + ": '" + a + "' @ " + currentState.ToString());
newState.Inherit(currentState);
newState.Step();
decimal r = GetReward(currentState, newState);
score += r;
QUpdate(actionsTaken, currentState, a, newState, r);
WriteOutput((CurrentMode == LEARN ? "Trial " + trialNum + ", " : "") + "#" + actionsTaken + " Gain " + Math.Round(r, 4) + ", Total " + Math.Round(score, 4));
foreach (KeyValuePair <QStateActionPair, QState> kv in newState.GetObservedStates(currentState, a))
{
QState observedPriorState = kv.Key.state;
QAction observedAction = kv.Key.action;
QState observedState = kv.Value;
decimal observedR = GetReward(observedPriorState, observedState);
QUpdate(actionsTaken, observedPriorState, observedAction, observedState, observedR);
WriteOutput((CurrentMode == LEARN ? "Trial " + trialNum + ", " : "") + "#" + actionsTaken + " Observed: '" + observedAction + "' @ " + observedPriorState.ToString() + " | Gain " + Math.Round(observedR, 4));
}
currentState = newState;
}
if (isRunning)
{
WriteOutput("Trial " + trialNum + ": " + Math.Round(score, 4) + " in " + actionsTaken + " step" + (actionsTaken == 1 ? "" : "s") + ".");
}
return(currentState);
}
private void Run(decimal learn, decimal discount, decimal explore)
{
trialNum++;
this.learn = learn;
this.discount = discount;
this.explore = explore;
if (master != null)
{
master.UpdateTrial(trialNum);
}
QState state = initialState.Initialize();
state.Inherit(initialState);
QState s = currentAlgo.Run(state, trialNum, learn, discount, explore);
if (master != null)
{
master.UpdateScore(s.GetValue());
master.UpdateGuiMetrics();
}
s.End();
}