//  [python]
def create_variables(self):
        with tf.name_scope("model_inputs"):
            self.states = tf.placeholder(dtype=tf.float64, shape=(None, 74, self.state_dim), name="states")

        # rollout action based on current policy
        with tf.name_scope("predict_actions"):
            # initialize actor-critic network
            with tf.variable_scope("actor_network"):
                self.policy_outputs, self.actor_output_state = self.actor_network(self.states)
            with tf.variable_scope("critic_network"):
                self.value_outputs = self.critic_network(self.states)

            # predict actions from policy network
            self.action_scores = tf.identity(self.policy_outputs, name="action_scores")
            self.predicted_actions = tf.random.categorical(self.action_scores, 1)

        # get variable list
        actor_network_variables  = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="actor_network")
        critic_network_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="critic_network")

        # compute loss and gradients
        with tf.name_scope("compute_pg_gradients"):
            # gradients for selecting action from policy network
            self.taken_actions = tf.placeholder(dtype=tf.int32, shape=(None,), name="taken_actions")
            self.discounted_rewards = tf.placeholder(dtype=tf.float64, shape=(None,), name="discounted_rewards")

            with tf.variable_scope("actor_network", reuse=True):
                self.logprobs, _ = self.actor_network(self.states)

            with tf.variable_scope("critic_network", reuse=True):
                self.estimated_values = self.critic_network(self.states)

            # compute policy loss and regularization loss
            self.cross_entropy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logprobs, labels=self.taken_actions)
            self.pg_loss = tf.reduce_mean(self.cross_entropy_loss)
            self.actor_reg_loss = tf.reduce_sum([tf.reduce_sum(tf.square(x)) for x in actor_network_variables])
            self.actor_loss = self.pg_loss + self.reg_param * self.actor_reg_loss

            # compute actor gradients
            self.actor_gradients = self.optimizer.compute_gradients(self.actor_loss, actor_network_variables)
            # compute advantages A(s) = R - V(s)
            self.advantages = tf.reduce_sum(self.discounted_rewards - self.estimated_values)
            # compute policy gradients
            for i, (grad, var) in enumerate(self.actor_gradients):
                if grad is not None:
                    self.actor_gradients[i] = (grad * self.advantages, var)

            # compute critic gradients
            self.mean_square_loss = tf.reduce_mean(tf.square(self.discounted_rewards - self.estimated_values))
            self.critic_reg_loss = tf.reduce_sum([tf.reduce_sum(tf.square(x)) for x in critic_network_variables])
            self.critic_loss = self.mean_square_loss + self.reg_param * self.critic_reg_loss
            self.critic_gradients = self.optimizer.compute_gradients(self.critic_loss, critic_network_variables)

            # collect all gradients
            self.gradients = self.actor_gradients + self.critic_gradients

            # gradients clipping by norm
            for i, (grad, var) in enumerate(self.gradients):
                if grad is not None:
                    self.gradients[i] = (tf.clip_by_norm(grad, self.max_gradient), var)

            # summarize gradients
            for grad, var in self.gradients:
                tf.summary.histogram(var.name, var)
                if grad is not None:
                    tf.summary.histogram(var.name + '/gradients', grad)

            # emit summaries
            tf.summary.histogram("estimated_values", self.estimated_values)
            tf.summary.scalar("actor_loss", self.actor_loss)
            tf.summary.scalar("critic_loss", self.critic_loss)
            tf.summary.scalar("reg_loss", self.actor_reg_loss + self.critic_reg_loss)

        # training update
        with tf.name_scope("train_actor_critic"):
            # apply gradients to update actor network
            self.train_op = self.optimizer.apply_gradients(self.gradients)

        self.summarize = tf.summary.merge_all()
        self.no_op = tf.no_op()