Performs update on model.
        :param mini_batch: Batch of experiences.
        :param num_sequences: Number of sequences to process.
        :return: Results of update.

def update(self, mini_batch, num_sequences):
        """
        Performs update on model.
        :param mini_batch: Batch of experiences.
        :param num_sequences: Number of sequences to process.
        :return: Results of update.
        """

        feed_dict = {self.model.dropout_rate: self.update_rate,
                     self.model.batch_size: num_sequences,
                     self.model.sequence_length: self.sequence_length}
        if self.use_continuous_act:
            feed_dict[self.model.true_action] = mini_batch['actions']. \
                reshape([-1, self.brain.vector_action_space_size[0]])
        else:
            feed_dict[self.model.true_action] = mini_batch['actions'].reshape(
                [-1, len(self.brain.vector_action_space_size)])
            feed_dict[self.model.action_masks] = np.ones(
                (num_sequences, sum(self.brain.vector_action_space_size)))
        if self.use_vec_obs:
            apparent_obs_size = self.brain.vector_observation_space_size * \
                                self.brain.num_stacked_vector_observations
            feed_dict[self.model.vector_in] = mini_batch['vector_obs'] \
                .reshape([-1,apparent_obs_size])
        for i, _ in enumerate(self.model.visual_in):
            visual_obs = mini_batch['visual_obs%d' % i]
            feed_dict[self.model.visual_in[i]] = visual_obs
        if self.use_recurrent:
            feed_dict[self.model.memory_in] = np.zeros([num_sequences, self.m_size])
        run_out = self._execute_model(feed_dict, self.update_dict)
        return run_out