--- a
+++ b/ADDPG/main.py
@@ -0,0 +1,73 @@
+from model import *
+import sys
+import os
+import multiprocessing
+import threading
+import argparse
+worker_threads = []
+
+def main():
+
+    parser = argparse.ArgumentParser(description='Train or test neural net motor controller')
+    parser.add_argument('--load_model', dest='load_model', action='store_true', default=False)
+    parser.add_argument('--num_workers', dest='num_workers',action='store',default=3,type=int)
+    parser.add_argument('--visualize', dest='vis', action='store_true', default=False)
+    args = parser.parse_args()
+
+    load_model = args.load_model
+    num_workers = args.num_workers
+    vis = args.vis
+    training = True#not load_model
+    model_path = './models'
+
+    if not os.path.exists(model_path):
+        os.makedirs(model_path)
+	
+    # hyperparameters
+    explore = 1000
+    batch_size = 32
+    gamma = 0.995
+    n_step = 3
+        
+    tf.reset_default_graph()
+        
+    with tf.Session() as sess:
+	    with tf.device("/cpu:0"): 
+           	global_episodes = tf.Variable(0,dtype=tf.int32,name='global_episodes',trainable=False)
+	        global_actor_network = ActorNetwork(sess,41+14+3,18,'global'+'/actor')
+	        num_cpu = multiprocessing.cpu_count() # Set workers ot number of available CPU threads
+	        workers = []
+	        # Create worker classes
+	        for i in range(num_workers):
+	            worker = Worker(sess,i,model_path,global_episodes,explore,training,vis,batch_size,gamma,n_step,global_actor_network.net)
+		    workers.append(worker)
+	        saver = tf.train.Saver()
+
+            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)
+            	print ('Loading Model succeeded...')
+            else:
+                sess.run(tf.global_variables_initializer())
+                
+            # This is where the asynchronous magic happens.
+            # Start the "work" process for each worker in a separate thread.
+            
+            for worker in workers:
+                worker_work = lambda: worker.work(coord,saver)
+                t = threading.Thread(target=(worker_work))
+                t.daemon = True
+                t.start()
+                worker_threads.append(t)
+                sleep(0.05)
+            coord.join(worker_threads)
+        
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("Ctrl-c received! Sending kill to threads...")
+        for t in worker_threads:
+            t.kill_received = True