public void LearnPS(int N, int episodes)
{
subgoalVar = new List <double>();
forceStop = false;
//int stateHold = currentState;
float alpha = 0.2f, gamma = 0.95f, theta = 0.001f;
int s = currentState; //Initialize
List <PriorityObj> PQueue = new List <PriorityObj>();
visit = new Dictionary <int, int>();
int epsilon_counter = 0;
int steps = 0;
while (true)
{
if (forceStop)
{
break;
}
if (epsilon_counter++ % 100 == 0)
{
epsilon_counter = 1;
e = 0.01 + 0.7 * Math.Pow(Math.E, -(discount++) / 20.0);
}
int tempSteps = steps; //Number of steps per episode
if (EpisodeSteps.Count > 1500)
{
forceStop = true;
}
//(b) a <- policy(s,Q)
//int a = e_greedy(Q[s]);
int a = softmaxWVisits(Q[s]);
// Q[s][a] -= -0.001;
double tempVal = Q[s][a];
//tempFirstVisitHist[s[0], s[1]] += 1;
//(c) Execute action a
int sP = 0;
double r = 0;
Context curCtxt = currentContext;
lock (threadLock)
{
executeAction(s, a, ref sP, ref r, ref steps);
}
if (!visit.ContainsKey(sP))
{
visit.Add(sP, 0);
}
Context nexCtxt = currentContext;
if (curCtxt != nexCtxt)
{
//double tmp = curCtxt.Q[s][Q[s].GetMaxAction()];
//for (int i = 0; i < curCtxt.Q[s].Actions.Count; i++)
//{
// Q[s][i] += curCtxt.Q[s][i];//tmp / 8;
//}
continue;
}
//SwitchContext(curCtxt);
int result = sP;
//(d) Model(s,a) <- s', r
//Model[s[0], s[1], a] = new int[] { sP[0], sP[1], r, steps - tempSteps };
if (Model[s, a] == null)
{
ModelValue m = new ModelValue(s, a, sP, r, steps - tempSteps);
Model[s, a] = m;
}
else
if (Model[s, a].calculateEm(sP, r) > 0)
{
Model[s, a].Update(s, a, sP, r, steps - tempSteps);
}
//System.Diagnostics.Debug.WriteLine("\tContext: " + currentContext.cID + "\t(s,a,sp): " + s + ", " + a + ", " + sP + "\tTm0: " /*+ (Model[s, a].Tm.Count > 0 ? Model[s,a].Tm[0].ToString() : " ") */ + "\tem0: " + em + "\tEm0: " + contexts[0].Em + "\tEm1: " + (contexts.Count > 1 ? contexts[1].Em.ToString() : "-"));
//(e) p <- |r + gama*maxA'Q(s',a') - Q(s,a)|
//float p = Math.Abs(r + gamma * getQ(sP[0], sP[1], maxA(sP)) - getQ(s[0], s[1], a));
double p = Math.Abs(r + gamma * Q[sP][Q[sP].GetMaxAction()] - Q[s][a]);
//(f) if p > tetha, then insert s,a into PQueue with priority p
PQueue.Clear();
if (p > theta)
{
InsertQueue(PQueue, s, a, p);
}
//(g) Repeat N times while PQueue is not empty
for (int i = 0; i < N && 0 < PQueue.Count; i++)
{
//(-)s, a <- first(PQueue)
PriorityObj obj = PQueue[0];// dene!!!!!!!!!!!!11
PQueue.Remove(obj);
s = obj.State;
a = obj.Action;
//(-)s', r <- Modell(s,a)
sP = Model[s, a].sP;
r = Model[s, a].reward;
int tsteps = Model[s, a].steps;
//(-)Q(s, a) <- Q(s,a) + alpha[r + gama*maxA'Q(s',a') - Q(s,a)]
//e_updates.Add(new double[] { s, a, Q[s][a] });
Q[s][a] = Q[s][a] + alpha * (r + Math.Pow(gamma, tsteps) * Q[sP][Q[sP].GetMaxAction()] - Q[s][a]);
//float t = (float)(getQ(s[0], s[1], a) + alpha * (r + Math.Pow(gamma, tsteps) * getQ(sP[0], sP[1], maxA(sP)) - getQ(s[0], s[1], a)));
//setQ(s[0], s[1], a, t);
//(-)Repeat, for all s",a" predicted to lead to s
List <ModelValue> list = Model.All_SA_PredictedToLeadTo(s);
for (int j = 0; j < list.Count; j++)
{
int sDP = list[j].s;
int aDP = list[j].a;
// r" <- predicted reward
double rDP = list[j].reward;
// p <- |r" + gamma*maxaQ(s,a) - Q(s",a")|
p = Math.Abs(rDP + gamma * Q[s][Q[s].GetMaxAction()] - Q[sDP][aDP]);
// if p > tetha, then insert s",a" into PQueue with priority p
if (p > theta)
{
InsertQueue(PQueue, sDP, aDP, p);
}
}
}
s = result;
environment.State = s;
if (environment.isGoal(s))
{
//visit = new Dictionary<int, int>();///////////
//if (subgoalValues != null)
//{
// subgoalValues[s] = 0.1;
// for (int f = 0; f < 4; f++)
// {
// if (Model.StateCount < s)
// Model.States.Add(new ModelState(s));
// if (Model[s] == null)
// Model[s] = new ModelState(s);
// if(Model[s].Count <= 4)
// Model[s].Add(new ModelValue(s, f, s, 0, 0));
// }
//}
if (Model.States.Count <= s)
{
Model[s, a] = null;
Model.States[s] = new ModelState(s);
}
//e = 0.9 * Math.Pow(Math.E, -(discount++) / 50.0); //Update epsilon
if (!DisableOptions && !m_optsLearned)
{
//initiationSetMembers = new int[Model.StateCount];
//initiationSets = new List<InitiationSet>();
Subgoals = SearchSubgoals();
//Subgoals.Add(new SubGoal(s));
SubgoalCounts.Add(Subgoals.Count);
double mean = 0;
double std = 100;
int sc = 10;
if (SubgoalCounts.Count > sc)
{
std = 0;
for (int c = SubgoalCounts.Count - sc; c < SubgoalCounts.Count; c++)
{
mean += SubgoalCounts[c] / (double)sc;
}
for (int c = SubgoalCounts.Count - sc; c < SubgoalCounts.Count; c++)
{
std += Math.Pow(SubgoalCounts[c] - mean, 2) / sc;
}
subgoalVar.Add(std);
std = Math.Sqrt(std);
}
else
{
subgoalVar.Add(-1);
}
if (std < 0.01)
{
List <OptionN> options = createOptFromSubGoals(Subgoals);
currentContext.options = options;
optNonLearnedVals = 0;
optLearnedVals = 0;
for (int i = 0; i < Model.StateCount; i++)
{
if (Model[i] == null)
{
optNonLearnedVals += 4;
}
else
{
for (int j = 0; j < Model[i].Count; j++)
{
if (Model[i][j] == null || Q[i][j] == 0)
{
optNonLearnedVals += 1;
}
else
{
optLearnedVals += 1;
}
}
}
}
//SearchInitiationSets(Subgoals); //Bu satır if in içinde olmalı (ki öyle(değil mi?))
//List<Option> options = CreateOptions();
OptionLearn(options);
//currentContext.options = options;
}
}
s = currentState;
EpisodeSteps.Add(steps);
ContextTrack.Add(currentContext.cID);
currentContext.EpisodeSteps.Add(steps);
steps = 0;
int calcLength = 9;
if (currentContext.EpisodeSteps.Count > calcLength)
{
double mean = 0;
double std = 0;
for (int c = currentContext.EpisodeSteps.Count - calcLength; c < currentContext.EpisodeSteps.Count; c++)
{
mean += EpisodeSteps[c] / (double)calcLength;
}
for (int c = currentContext.EpisodeSteps.Count - calcLength; c < currentContext.EpisodeSteps.Count; c++)
{
std += Math.Pow(currentContext.EpisodeSteps[c] - mean, 2) / calcLength;
}
std = Math.Sqrt(std);
currentContext.std = std;
bool stop = true;
for (int cx = 0; cx < contexts.Count; cx++)
{
if (contexts[cx].std > 10)
{
stop = false;
}
//System.Diagnostics.Debug.Write(cx + ":" + contexts[cx].std + " ");
}
//System.Diagnostics.Debug.Write("\n");
if (stopByEpisode)
{
if (stopEpisode <= EpisodeSteps.Count)
{
break;
}
}
else
{
if (stop || forceStop)
{
break;
}
}
}//Varyans ile dene
//if (!m_optsLearned)
//{
// OptionSearch();
// OptionLearn();
//}
PQueue.Clear();
if (curCtxt != nexCtxt)
{
SwitchContext(nexCtxt);
}
if (oneStepLearn)
{
break;
}
e_updates = new List <double[]>();
}
}
environment.State = currentState;
}
