public void RunTraining()
{
QMethod.Validate(this);
/*
* For each episode: Select random initial state
* Do while not reach goal state
* Select one among all possible actions for the current state
* Using this possible action, consider to go to the next state
* Get maximum Q value of this next state based on all possible actions
* Set the next state as the current state
*/
// For each episode
var rand = new Random();
long maxloopEventCount = 0;
// Train episodes
for (long i = 0; i < Episodes; i++)
{
long maxloop = 0;
// Select random initial state
int stateIndex = rand.Next(States.Count);
QState state = States[stateIndex];
QAction action = null;
do
{
if (++maxloop > MaxExploreStepsWithinOneEpisode)
{
if (ShowWarning)
{
string msg = string.Format(
"{0} !! MAXLOOP state: {1} action: {2}, {3} endstate is to difficult to reach?",
++maxloopEventCount, state, action, "maybe your path setup is wrong or the ");
QMethod.Log(msg);
}
break;
}
// no actions, skip this state
if (state.Actions.Count == 0)
{
break;
}
// Selection strategy is random based on probability
int index = rand.Next(state.Actions.Count);
action = state.Actions[index];
// Using this possible action, consider to go to the next state
// Pick random Action outcome
QActionResult nextStateResult = action.PickActionByProbability();
string nextStateName = nextStateResult.StateName;
double q = nextStateResult.QEstimated;
double r = nextStateResult.Reward;
double maxQ = MaxQ(nextStateName);
// Q(s,a)= Q(s,a) + alpha * (R(s,a) + gamma * Max(next state, all actions) - Q(s,a))
double value = q + Alpha * (r + Gamma * maxQ - q); // q-learning
nextStateResult.QValue = value; // update
// is end state go to next episode
if (EndStates.Contains(nextStateResult.StateName))
{
break;
}
// Set the next state as the current state
state = StateLookup[nextStateResult.StateName];
} while (true);
}
}
public void AddActionResult(QActionResult actionResult)
{
ActionsResult.Add(actionResult);
}
