--- a
+++ b/A3C/helper.py
@@ -0,0 +1,88 @@
+import numpy as np
+import tensorflow as tf
+import scipy.signal as ss
+
+
+# Helper Function------------------------------------------------------------------------------------------------------------
+# Copies one set of variables to another.
+# Used to set worker network parameters to those of global network.
+def update_target_graph(from_scope,to_scope):
+    from_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, from_scope)
+    to_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, to_scope)
+
+    op_holder = []
+    for from_var,to_var in zip(from_vars,to_vars):
+        op_holder.append(to_var.assign(from_var))
+    return op_holder
+
+# Normalize state 
+def process_frame(s):
+    s = np.asarray(s)
+    s = (s-np.mean(s)) / np.std(s)
+    return s
+
+# process state (the last 3 entires are obstacle info which should not be processed)
+def process_state(s,s1):
+    s = np.asarray(s)
+    s1 = np.asarray(s1)
+    s = np.hstack((s1[:-3]-s[:-3],s[-3:]))
+    return s
+    
+def engineered_action(seed):
+    a = np.ones(18)*0.05
+    if seed < .5:
+        a[17:]=0.9
+        a[0]=0.9
+        a[3]=0.9
+        a[4]=0.9
+        a[8]=0.9
+        a[11]=0.9
+        a[12]=0.9
+        a[13]=0.9
+        a[10]=0.9
+    else:
+        a[8]=0.9
+        a[9]=0.9
+        a[12]=0.9
+        a[13]=0.9
+        a[17]=0.9
+        a[2]=0.9
+        a[3]=0.9
+        a[4]=0.9
+        a[1]=0.9 
+    return a
+
+def normalized_columns_initializer(std=1.0):
+    def _initializer(shape, dtype=None, partition_info=None):
+        out = np.random.randn(*shape).astype(np.float32)
+        out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
+        return tf.constant(out)
+    return _initializer
+
+def discount(x, gamma):
+    return ss.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]
+
+#These functions allows us to update the parameters of our target network with those of the primary network.
+def updateTargetGraph(tfVars,tau):
+    total_vars = len(tfVars)
+    op_holder = []
+    for idx,var in enumerate(tfVars[0:total_vars/2]):
+        op_holder.append(tfVars[idx+total_vars//2].assign((var.value()*tau) + ((1-tau)*tfVars[idx+total_vars//2].value())))
+    return op_holder
+
+def updateTarget(op_holder,sess):
+    for op in op_holder:
+        sess.run(op)
+    total_vars = len(tf.trainable_variables())
+    a = tf.trainable_variables()[0].eval(session=sess)
+    b = tf.trainable_variables()[total_vars/2].eval(session=sess)
+    if a.all() == b.all():
+        print("Target Set Success")
+    else:
+        print("Target Set Failed")
+        
+
+    
+
+
+