import tensorflow as tf

                        if '_' in self.ent_coef:
                            init_value = float(self.ent_coef.split('_')[1])
                            assert init_value > 0., "The initial value of ent_coef must be greater than 0"

                        self.log_ent_coef = tf.get_variable('log_ent_coef', dtype=tf.float32,
                                                            initializer=np.log(init_value).astype(np.float32))
                        self.ent_coef = tf.exp(self.log_ent_coef)
                    else:
                        # Force conversion to float
                        # this will throw an error if a malformed string (different from 'auto')
                        # is passed
                        self.ent_coef = float(self.ent_coef)

                with tf.variable_scope("target", reuse=False):
                    # Create the value network
                    _, _, value_target = self.target_policy.make_critics(self.processed_next_obs_ph,
                                                                         create_qf=False, create_vf=True)
                    self.value_target = value_target
                    if self.n_step:
                        _,_,value_target_n = self.policy_tf.make_critics(self.processed_next_obs_ph_n,
                                                                     create_qf=False, create_vf=True,reuse=True)
                        self.value_target_n = value_target_n

                with tf.variable_scope("loss", reuse=False):
                    # Take the min of the two Q-Values (Double-Q Learning)
                    min_qf_pi = tf.minimum(qf1_pi, qf2_pi)
