async_knowledge_transfer.py 文件源码

def build_networks(self):
        with tf.variable_scope("shared"):
            self.states = tf.placeholder(tf.float32, [None] + list(self.envs[0].observation_space.shape), name="states")
            self.action_taken = tf.placeholder(tf.float32, name="action_taken")
            self.advantage = tf.placeholder(tf.float32, name="advantage")

            if self.config["feature_extraction"]:
                self.L1 = tf.contrib.layers.fully_connected(
                    inputs=self.states,
                    num_outputs=self.config["n_hidden_units"],
                    activation_fn=tf.tanh,
                    weights_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.02),
                    biases_initializer=tf.zeros_initializer(),
                    scope="L1")
            else:
                self.L1 = self.states
            self.knowledge_base = tf.Variable(tf.truncated_normal([self.L1.get_shape()[-1].value, self.config["n_sparse_units"]], mean=0.0, stddev=0.02), name="knowledge_base")

            self.shared_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)