static QState ArgMax(Dictionary <QState, double> values)
{
QState max = values.First().Key;
foreach (var state in values)
{
if (state.Value > values[max])
{
max = state.Key;
}
}
return(max);
}
public void Train(QState goal, int iterations, double learningRate, double discountRate)
{
var rng = new Random();
for (int epoch = 0; epoch < iterations; ++epoch)
{
foreach (var item in initialStateProvider)
{
item.SetReward();
}
QState prevState = null;
var state = initialStateProvider[0];
QState nextState = state.GetRandNextState(null);
QState nextNextState = null;
while (true)
{
List <QState> possNextNextStates = nextState.GetAvailableStates();
double maxQ = double.MinValue;
double maxNrAp = double.MaxValue;
for (int j = 0; j < possNextNextStates.Count; ++j)
{
var possNextState = possNextNextStates[j];
double q = Q[nextState][possNextState];
if (q > maxQ)
{
maxQ = q;
maxNrAp = (Nsa[nextState][possNextState] - 1) * 1000;
nextNextState = possNextState;
}
}
Nsa[state][nextState] += 0.004;
Q[state][nextState] = Q[state][nextState] + learningRate / Nsa[state][nextState] * (state.GetReward().ComputeReward() + discountRate * maxQ - 1 * Q[state][nextState]);
if (state.Equals(goal))
{
Q[state][state] = state.GetReward().ComputeReward(); break;
}
prevState = state;
state = nextState;
nextState = nextNextState;
}
}
}
public List <QState> Walk(QState start, QState goal)
{
const int maxListLen = 300;
List <QState> result = new List <QState>();
result.Add(start);
QState current = start; QState next;
Console.Write(current.ToString() + "->");
while (!current.Equals(goal))
{
next = ArgMax(Q[current]);
Console.Write(next + "->");
result.Add(next);
current = next;
if (result.Count == maxListLen)
{
break;
}
}
return(result);
}
public abstract QState GetRandNextState(QState prevState);
