import tensorflow as tf

                                     trainable=trainable
                                     )
            sigma = tf.layers.dense(inputs=l1,
                                    units=action_dim,  # output units
                                    activation=tf.nn.softplus,  # get action probabilities
                                    name='sigma',
                                    trainable=trainable
                                    )
            norm_dist = tf.distributions.Normal(loc=mu, scale=sigma)
        params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
        return norm_dist, params

    def choose_action(self, s):
        # 決定下一步該怎麼做
        # s = s[np.newaxis, :]
        s = s.reshape(-1, 84, 84, 3)
        a = self.sess.run(self.sample_op, {self.tfs: s})[0]
        return np.clip(a, ACTION_BOUND[0], ACTION_BOUND[1])
