getting right starter files

pacmanAgents.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
 # ([email protected]) and Dan Klein ([email protected]).
@@ -18,20 +18,27 @@ import random
 import game
 import util
 
+
 class LeftTurnAgent(game.Agent):
     "An agent that turns left at every opportunity"
 
     def getAction(self, state):
         legal = state.getLegalPacmanActions()
         current = state.getPacmanState().configuration.direction
-        if current == Directions.STOP: current = Directions.NORTH
+        if current == Directions.STOP:
+            current = Directions.NORTH
         left = Directions.LEFT[current]
-        if left in legal: return left
-        if current in legal: return current
-        if Directions.RIGHT[current] in legal: return Directions.RIGHT[current]
-        if Directions.LEFT[left] in legal: return Directions.LEFT[left]
+        if left in legal:
+            return left
+        if current in legal:
+            return current
+        if Directions.RIGHT[current] in legal:
+            return Directions.RIGHT[current]
+        if Directions.LEFT[left] in legal:
+            return Directions.LEFT[left]
         return Directions.STOP
 
+
 class GreedyAgent(Agent):
     def __init__(self, evalFn="scoreEvaluation"):
         self.evaluationFunction = util.lookup(evalFn, globals())
@@ -40,13 +47,17 @@ class GreedyAgent(Agent):
     def getAction(self, state):
         # Generate candidate actions
         legal = state.getLegalPacmanActions()
-        if Directions.STOP in legal: legal.remove(Directions.STOP)
+        if Directions.STOP in legal:
+            legal.remove(Directions.STOP)
 
-        successors = [(state.generateSuccessor(0, action), action) for action in legal]
-        scored = [(self.evaluationFunction(state), action) for state, action in successors]
+        successors = [(state.generateSuccessor(0, action), action)
+                      for action in legal]
+        scored = [(self.evaluationFunction(state), action)
+                  for state, action in successors]
         bestScore = max(scored)[0]
         bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
         return random.choice(bestActions)
 
+
 def scoreEvaluation(state):
     return state.getScore()