true action used

        # taking the most likely action.

        # use the hyperparameter nu_pretrain to take the true action

        # or the one take from the one computed from the q_target

        if self.dev_network_count < self.nu_pretrain:

            #print ("Using pretraining...")

        if self.learn_step_counter % 50 == 1:

            print ("learn step counter: ", self.learn_step_counter)

            print ("dev_network_count: ", self.dev_network_count )

        if self.learn_step_counter < self.nu_pretrain:

            if self.learn_step_counter == 1:

                print ("Using pretraining...")

            b_a_ = torch.LongTensor(b_memory[:, self.state_size+2 + self.state_size]).view(self.sample_size, 1)

        else:

            if self.dev_network_count == self.nu_pretrain:

            if self.learn_step_counter == self.nu_pretrain:

                print ("Starting using Q target net....")

            b_a_ = torch.LongTensor(q_next.max(1)[1].view(self.sample_size, 1).long())

        #b_a_ = q_next.max(1)[0].view(self.sample_size, 1).long()   # shape (batch, 1)

        q_eval_next = self.eval_net(b_s_).gather(1, b_a_)   # shape (batch, 1)