import tensorflow as tf

                    # Create the policy
                    # first return value corresponds to deterministic actions
                    # policy_out corresponds to stochastic actions, used for training
                    # logp_pi is the log probability of actions taken by the policy
                    self.deterministic_action, policy_out, logp_pi = self.policy_tf.make_actor(self.processed_obs_ph)
                    # Monitor the entropy of the policy,
                    # this is not used for training
                    self.entropy = tf.reduce_mean(self.policy_tf.entropy)
                    self.obs_ph, self.actions_ph, self.deterministic_actions_ph = self._get_pretrain_placeholders()
                    
                    #  Use two Q-functions to improve performance by reducing overestimation bias.
                    qf1, qf2, value_fn = self.policy_tf.make_critics(self.processed_obs_ph, self.actions_ph,
                                                                     create_qf=True, create_vf=True)
                    qf1_pi, qf2_pi, _ = self.policy_tf.make_critics(self.processed_obs_ph,
