import tensorflow as tf

        rnd_pred_cri_in_dim = rnd_pred_cri_in_ph.shape.as_list()[1]
        rnd_pred_cri_dropout_mask_generator = DropoutMaskGenerator(rnd_pred_cri_in_dim,
                                                                   hidden_sizes, model_prob=1.0 - dropout_rate)
        rnd_pred_cri_dropout_mask_phs = rnd_pred_cri_dropout_mask_generator.generate_dropout_mask_placeholders()

        rnd_pred_cri, rnd_pred_cri_reg = mlp_variational(rnd_pred_cri_in_ph, rnd_pred_cri_dropout_mask_phs,
                                                         list(hidden_sizes) + [1],
                                                         activation, None, dropout_rate)
        rnd_pred_cri = tf.squeeze(rnd_pred_cri, axis=2)
    return rnd_targ_act,\
           rnd_pred_act, rnd_pred_act_reg, rnd_pred_act_dropout_mask_generator, rnd_pred_act_dropout_mask_phs,\
           rnd_targ_cri,\
           rnd_pred_cri, rnd_pred_cri_reg, rnd_pred_cri_dropout_mask_generator, rnd_pred_cri_dropout_mask_phs

"""
Actor-Critics
