make_worker.py

with tf.device("/cpu:0"): 
    master_network = AC_Network(s_size,a_size,'global',None) # Generate global network
    num_workers = multiprocessing.cpu_count() # Set workers ot number of available CPU threads
    workers = []
    # Create worker classes
    for i in range(num_workers):
        workers.append(Worker(DoomGame(),i,s_size,a_size,trainer,saver,model_path))

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    if load_model == True:
        print 'Loading Model...'
        ckpt = tf.train.get_checkpoint_state(model_path)
        saver.restore(sess,ckpt.model_checkpoint_path)
    else:
        sess.run(tf.global_variables_initializer())
        
    # This is where the asynchronous magic happens.
    # Start the "work" process for each worker in a separate threat.
    worker_threads = []
    for worker in workers:
        worker_work = lambda: worker.work(max_episode_length,gamma,master_network,sess,coord)
        t = threading.Thread(target=(worker_work))
        t.start()
        worker_threads.append(t)
    coord.join(worker_threads)