attempt at prioritized

analysis.py

@@ -4,7 +4,7 @@
 # educational purposes provided that (1) you do not distribute or publish
 # solutions, (2) you retain this notice, and (3) you provide clear
 # attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
-# 
+#
 # Attribution Information: The Pacman AI projects were developed at UC Berkeley.
 # The core projects and autograders were primarily created by John DeNero
 # (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
@@ -21,50 +21,57 @@
 
 def question2():
     answerDiscount = 0.9
-    answerNoise = 0.2
+    answerNoise = 0
     return answerDiscount, answerNoise
 
+
 def question3a():
-    answerDiscount = None
-    answerNoise = None
-    answerLivingReward = None
+    answerDiscount = .1
+    answerNoise = 0
+    answerLivingReward = .8
     return answerDiscount, answerNoise, answerLivingReward
     # If not possible, return 'NOT POSSIBLE'
 
+
 def question3b():
-    answerDiscount = None
-    answerNoise = None
-    answerLivingReward = None
+    answerDiscount = .1
+    answerNoise = .2
+    answerLivingReward = .8
     return answerDiscount, answerNoise, answerLivingReward
     # If not possible, return 'NOT POSSIBLE'
 
+
 def question3c():
-    answerDiscount = None
-    answerNoise = None
-    answerLivingReward = None
+    answerDiscount = .5
+    answerNoise = 0
+    answerLivingReward = .5
     return answerDiscount, answerNoise, answerLivingReward
     # If not possible, return 'NOT POSSIBLE'
 
+
 def question3d():
-    answerDiscount = None
-    answerNoise = None
-    answerLivingReward = None
+    answerDiscount = .9
+    answerNoise = .1
+    answerLivingReward = .2
     return answerDiscount, answerNoise, answerLivingReward
     # If not possible, return 'NOT POSSIBLE'
 
+
 def question3e():
-    answerDiscount = None
-    answerNoise = None
-    answerLivingReward = None
+    answerDiscount = 0
+    answerNoise = 0
+    answerLivingReward = 0
     return answerDiscount, answerNoise, answerLivingReward
     # If not possible, return 'NOT POSSIBLE'
 
+
 def question8():
     answerEpsilon = None
     answerLearningRate = None
     return answerEpsilon, answerLearningRate
     # If not possible, return 'NOT POSSIBLE'
 
+
 if __name__ == '__main__':
     print('Answers to analysis questions:')
     import analysis