Action QLearner::getAction(const State &state)
{
Action nextAction;
if(getValue(state) < someValue)
nextAction = static_cast<Action>(rand() % 3);
else
nextAction = getBestAction(state);
return nextAction;
}
LearningProblemState*
QLearner::doLearningIteration(LearningProblemState * state)
{
// Pick a new state once in a while
if (randomReal() < nu) {
state = problem->getRandomState();
}
// Get the list of actions
LearningProblemAction* actions = problem->getActions(state);
LearningProblemAction* action = NULL;
// Check if we should use a random action, or the best one
if (randomReal() < rho) {
unsigned randPos = randomInt(actions->getCount());
action = actions->getAtPositionInList(randPos);
} else {
action = getBestAction(state);
}
// Make sure we've got something to do
if (action != NULL)
{
// Carry out the action
LearningProblemActionResult result =
problem->getResult(state, action);
// Get the current q value
real q = getQValue(state, action);
// Get the q of the best action from the new state
real maxQ = getBestQValue(result.state);
// recalculate the q
q = ((real)1.0-alpha) * q + alpha * (result.reward + gamma * maxQ);
// Store the new Q value
storeQValue(state, action, q);
return result.state;
}
// Otherwise we need to get a new state - we've reached the
// end of the road.
else
{
return problem->getRandomState();
}
}
