import tensorflow as tf

                                                                   hidden_sizes, model_prob=1.0 - dropout_rate)
        rnd_pred_act_dropout_mask_phs = rnd_pred_act_dropout_mask_generator.generate_dropout_mask_placeholders()
        rnd_pred_act, rnd_pred_act_reg = mlp_variational(x_ph, rnd_pred_act_dropout_mask_phs,
                                                         list(hidden_sizes) + [act_dim], activation,
                                                         output_activation, dropout_rate)
        rnd_pred_act = act_limit * rnd_pred_act

    with tf.variable_scope('rnd_targ_cri'):
        rnd_targ_cri = tf.squeeze(mlp(tf.concat([x_ph, a_ph], axis=-1), list(hidden_sizes) + [1], activation, None), axis=1)
    with tf.variable_scope('rnd_pred_cri'):
        rnd_pred_cri = tf.squeeze(mlp(tf.concat([x_ph, a_ph], axis=-1), list(hidden_sizes) + [1], activation, None),
                                  axis=1)
        rnd_pred_cri_in_ph = tf.concat([x_ph, a_ph], axis=-1)
        rnd_pred_cri_in_dim = rnd_pred_cri_in_ph.shape.as_list()[1]
