import tensorflow as tf

        self.actions = tf.placeholder(tf.float32, [None, self.a_dim], 'action')
        self.state = tf.placeholder(tf.float32, [None, self.s_dim[0]], 'state')
        self.advantage = tf.placeholder(tf.float32, [None, 1], 'advantage')
        self.rewards = tf.placeholder(tf.float32, [None, 1], 'rewards')
        self.keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')
        # Dateset with experiennce replay
        self.dataset = tf.data.Dataset.from_tensor_slices({'state': self.state, 'actions': self.actions,
                                                           'rewards': self.rewards, 'advantage': self.advantage})
        self.dataset = self.dataset.batch(self.MINIBATCH, drop_remainder=True)
        self.data_iter = self.dataset.make_initializable_iterator()
        batch = self.data_iter.get_next()
        # Call ppo net
