import tensorflow as tf

            assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the ACER model must be " \
                                                               "an instance of common.policies.ActorCriticPolicy."

            if isinstance(self.action_space, Discrete):
                self.n_act = self.action_space.n
                continuous = False
            elif isinstance(self.action_space, Box):
                # self.n_act = self.action_space.shape[-1]
                # continuous = True
                raise NotImplementedError("WIP: Acer does not support Continuous actions yet.")
            else:
                raise ValueError("Error: ACER does not work with {} actions space.".format(self.action_space))

            self.n_batch = self.n_envs * self.n_steps

            self.graph = tf.Graph()
            with self.graph.as_default():
                self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess, graph=self.graph)
                self.set_random_seed(self.seed)
                n_batch_step = None
                if issubclass(self.policy, RecurrentActorCriticPolicy):
                    n_batch_step = self.n_envs
                n_batch_train = self.n_envs * (self.n_steps + 1)

                step_model = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
                                         n_batch_step, reuse=False, **self.policy_kwargs)

                self.params = tf_util.get_trainable_vars("model")

                with tf.variable_scope("train_model", reuse=True,
