static int semi_uniform (RLearner *rl, const unsigned int state)
{
double rand_value = 0.0;
int action = 0;
if (rl == NULL)
{
fprintf(stderr, "RLearner-WARNING **: RLearner *rl is NULL\n");
}
else
{
rand_value = g_rand_double(rl->random);
if (rand_value > (1.0 - EXPLORATION_THRESHOLD))
{
/* Random action */
action = g_rand_int_range(rl->random, 0, rl->num_actions);
}
else
{
action = best_action(rl, state);
}
}
return action;
}
static double best_qvalue (RLearner *rl, const unsigned int state)
{
double best_value = 0;
if (rl == NULL)
{
fprintf(stderr, "RLearner-WARNING **: RLearner *rl is NULL\n");
}
else
{
best_value = rl->q_values[ state ][ best_action(rl, state) ];
}
return best_value;
}
// QUtility
void QUtility::learn (QState last_state, QAction action,
QState current_state, QAction credit)
{
QCredit new_util, max_util;
QAction a;
/* assert(action >= 0 && action < total_actions);
for (a = 0; a < total_actions; a++) {
QCredit util = utility(current_state, a);
if (max_util < util || a == 0)
max_util = util;
}*/
a = best_action(current_state);
max_util = utility(current_state, a);
new_util = credit + discount * max_util;
adjust(last_state, action, new_util);
}
void output_prefered_state_values (RLearner *rl)
{
int state = 0;
int action = 0;
if (rl == NULL)
{
fprintf(stderr, "RLearner-WARNING **: RLearner *rl is NULL\n");
}
else
{
for(state = 0; state < rl->num_states; state++)
{
printf("\n");
action = best_action (rl, state);
printf("R(%d,%d) = %05f ", state, action, rl->q_values[state][action]);
}
printf("\n");
}
}
QAction QUtility::select_action (QState state)
{
return best_action(state);
/* or, you can use Boltzmann here */
}
