train.py

class AC_Network():
    def __init__(self,s_size,a_size,scope,trainer):
        ....
        ....
        ....
        if scope != 'global':
            self.actions = tf.placeholder(shape=[None],dtype=tf.int32)
            self.actions_onehot = tf.one_hot(self.actions,a_size,dtype=tf.float32)
            self.target_v = tf.placeholder(shape=[None],dtype=tf.float32)
            self.advantages = tf.placeholder(shape=[None],dtype=tf.float32)

            self.responsible_outputs = tf.reduce_sum(self.policy * self.actions_onehot, [1])

            #Loss functions
            self.value_loss = 0.5 * tf.reduce_sum(tf.square(self.target_v - tf.reshape(self.value,[-1])))
            self.entropy = - tf.reduce_sum(self.policy * tf.log(self.policy))
            self.policy_loss = -tf.reduce_sum(tf.log(self.responsible_outputs)*self.advantages)
            self.loss = 0.5 * self.value_loss + self.policy_loss - self.entropy * 0.01

            #Get gradients from local network using local losses
            local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
            self.gradients = tf.gradients(self.loss,local_vars)
            self.var_norms = tf.global_norm(local_vars)
            grads,self.grad_norms = tf.clip_by_global_norm(self.gradients,40.0)

            #Apply local gradients to global network
            global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global')
            self.apply_grads = trainer.apply_gradients(zip(grads,global_vars))

class Worker():
      ....
      ....
      ....
      def train(self,global_AC,rollout,sess,gamma,bootstrap_value):
        rollout = np.array(rollout)
        observations = rollout[:,0]
        actions = rollout[:,1]
        rewards = rollout[:,2]
        next_observations = rollout[:,3]
        values = rollout[:,5]

        # Here we take the rewards and values from the rollout, and use them to 
        # generate the advantage and discounted returns. 
        # The advantage function uses "Generalized Advantage Estimation"
        self.rewards_plus = np.asarray(rewards.tolist() + [bootstrap_value])
        discounted_rewards = discount(self.rewards_plus,gamma)[:-1]
        self.value_plus = np.asarray(values.tolist() + [bootstrap_value])
        advantages = rewards + gamma * self.value_plus[1:] - self.value_plus[:-1]
        advantages = discount(advantages,gamma)

        # Update the global network using gradients from loss
        # Generate network statistics to periodically save
        rnn_state = self.local_AC.state_init
        feed_dict = {self.local_AC.target_v:discounted_rewards,
            self.local_AC.inputs:np.vstack(observations),
            self.local_AC.actions:actions,
            self.local_AC.advantages:advantages,
            self.local_AC.state_in[0]:rnn_state[0],
            self.local_AC.state_in[1]:rnn_state[1]}
        v_l,p_l,e_l,g_n,v_n,_ = sess.run([self.local_AC.value_loss,
            self.local_AC.policy_loss,
            self.local_AC.entropy,
            self.local_AC.grad_norms,
            self.local_AC.var_norms,
            self.local_AC.apply_grads],
            feed_dict=feed_dict)
        return v_l / len(rollout),p_l / len(rollout),e_l / len(rollout), g_n,v_n

class Worker():
....
....
....
deftrain(self,global_AC,rollout,sess,gamma,bootstrap_value)

Why do I need to pass global_AC to a function
It's not used, is it?