Creates state encoders for current and future observations.
        Used for implementation of ﻿Curiosity-driven Exploration by Self-supervised Prediction
        See https://arxiv.org/abs/1705.05363 for more details.
        :return: current and future state encoder tensors.

def create_curiosity_encoders(self):
        """
        Creates state encoders for current and future observations.
        Used for implementation of ﻿Curiosity-driven Exploration by Self-supervised Prediction
        See https://arxiv.org/abs/1705.05363 for more details.
        :return: current and future state encoder tensors.
        """
        encoded_state_list = []
        encoded_next_state_list = []

        if self.vis_obs_size > 0:
            self.next_visual_in = []
            visual_encoders = []
            next_visual_encoders = []
            for i in range(self.vis_obs_size):
                # Create input ops for next (t+1) visual observations.
                next_visual_input = self.create_visual_input(self.brain.camera_resolutions[i],
                                                             name="next_visual_observation_" + str(i))
                self.next_visual_in.append(next_visual_input)

                # Create the encoder ops for current and next visual input. Not that these encoders are siamese.
                encoded_visual = self.create_visual_observation_encoder(self.visual_in[i], self.curiosity_enc_size,
                                                                        self.swish, 1, "stream_{}_visual_obs_encoder"
                                                                        .format(i), False)

                encoded_next_visual = self.create_visual_observation_encoder(self.next_visual_in[i],
                                                                             self.curiosity_enc_size,
                                                                             self.swish, 1,
                                                                             "stream_{}_visual_obs_encoder".format(i),
                                                                             True)
                visual_encoders.append(encoded_visual)
                next_visual_encoders.append(encoded_next_visual)

            hidden_visual = tf.concat(visual_encoders, axis=1)
            hidden_next_visual = tf.concat(next_visual_encoders, axis=1)
            encoded_state_list.append(hidden_visual)
            encoded_next_state_list.append(hidden_next_visual)

        if self.vec_obs_size > 0:
            # Create the encoder ops for current and next vector input. Not that these encoders are siamese.
            # Create input op for next (t+1) vector observation.
            self.next_vector_in = tf.placeholder(shape=[None, self.vec_obs_size], dtype=tf.float32,
                                                 name='next_vector_observation')

            encoded_vector_obs = self.create_vector_observation_encoder(self.vector_in,
                                                                        self.curiosity_enc_size,
                                                                        self.swish, 2, "vector_obs_encoder",
                                                                        False)
            encoded_next_vector_obs = self.create_vector_observation_encoder(self.next_vector_in,
                                                                             self.curiosity_enc_size,
                                                                             self.swish, 2,
                                                                                 "vector_obs_encoder",
                                                                             True)
            encoded_state_list.append(encoded_vector_obs)
            encoded_next_state_list.append(encoded_next_vector_obs)

        encoded_state = tf.concat(encoded_state_list, axis=1)
        encoded_next_state = tf.concat(encoded_next_state_list, axis=1)
        return encoded_state, encoded_next_state