--- a
+++ b/A3C/main.py
@@ -0,0 +1,92 @@
+from model import *
+import argparse
+import sys
+import os
+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=1,type=int)
+    args = parser.parse_args()
+
+    max_episode_length = 1000
+    gamma = .995 # discount rate for advantage estimation and reward discounting
+    s_size = 41
+    a_size = 18 # Agent can move Left, Right, or Straight
+    model_path = './models'
+    load_model = args.load_model
+    noisy=False
+    num_workers = args.num_workers
+    print(" num_workers = %d" % num_workers)
+    print(" noisy_net_enabled = %s" % str(noisy))
+    print(" load_model = %s" % str(args.load_model))
+
+    tf.reset_default_graph()
+
+    if not os.path.exists(model_path):
+        os.makedirs(model_path)
+        
+
+    with tf.device("/cpu:0"): 
+        global_episodes = tf.Variable(0,dtype=tf.int32,name='global_episodes',trainable=False)
+        trainer = tf.train.AdamOptimizer(learning_rate=1e-4)
+        master_network = AC_Network(s_size,a_size,'global',None,noisy) # Generate global network
+        num_cpu = multiprocessing.cpu_count() # Set workers ot number of available CPU threads
+        workers = []
+            # Create worker classes
+        for i in range(args.num_workers):
+            worker = Worker(i,s_size,a_size,trainer,model_path,global_episodes,noisy,is_training= True)
+            workers.append(worker)
+
+        saver = tf.train.Saver()
+        
+    '''networks = ['global'] + ['worker_'+i for i in str(range(num_workers))]
+    print(networks)'''
+    #key = print_tensors_in_checkpoint_file('./tmp/checkpoints/mobilenet_v1_0.50_160.ckpt', tensor_name='',all_tensors=True)
+    #print(key)
+    
+    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)
+	    print('loading Model succeeded')
+        else:
+            '''
+            dict = {}
+            value = slim.get_model_variables('global'+'/MobilenetV1')
+            for variable in value:
+                name = variable.name.replace('global'+'/','').split(':')[0]
+                    #print(name)
+                if name in key:
+                    dict[name] = variable
+                #print(dict)
+                #print(dict)
+            init_fn = slim.assign_from_checkpoint_fn(
+                                os.path.join(checkpoints_dir, 'mobilenet_v1_0.50_160.ckpt'),
+                                dict)
+            init_fn(sess)'''
+            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(max_episode_length,gamma,sess,coord,saver)
+	    #worker.start(setting=0,vis=True)
+            t = threading.Thread(target=(worker_work))
+            t.daemon = True
+            t.start()
+            worker_threads.append(t)
+        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