# Reinforce Learning: Do I have to ignore hyper parameter(?) after training done in Q-learning?

Learner might be in training stage, where it update Q-table for bunch of epoch.

In this stage, Q-table would be updated with gamma(discount rate), learning rate(alpha), and action would be chosen by random action rate.

After some epoch, when reward is getting stable, let me call this "training is done". Then do I have to ignore these parameters(gamma, learning rate, etc) after that?

I mean, in training stage, I got an action from Q-table like this:

if rand_float < rar:
action = rand.randint(0, num_actions - 1)
else:
action = np.argmax(Q[s_prime_as_index])


But after training stage, Do I have to remove rar, which means I have to get an action from Q-table like this?

action = np.argmax(self.Q[s_prime])