game.py

@@ -650,9 +650,6 @@ class Game:
         agentIndex = self.startingIndex
         numAgents = len(self.agents)
 
-        time_sum = 0
-        counter = 0
-
         while not self.gameOver:
             # Fetch the next agent
             agent = self.agents[agentIndex]
@@ -732,13 +729,7 @@ class Game:
                     self.unmute()
                     return
             else:
-                start = time.time()
                 action = agent.getAction(observation)
-                stop = time.time()
-                time_sum += (stop-start)
-                counter += 1
-
-
             self.unmute()
 
             # Execute the action
@@ -771,8 +762,6 @@ class Game:
             if _BOINC_ENABLED:
                 boinc.set_fraction_done(self.getProgress())
 
-        print("TIME:", time_sum/counter)
-
         # inform a learning agent of the game result
         for agentIndex, agent in enumerate(self.agents):
             if "final" in dir(agent):

multiAgents.py

@@ -15,7 +15,6 @@
 from util import manhattanDistance
 from game import Directions
 import random, util
-import time
 
 from game import Agent
 from pacman import GameState
@@ -163,8 +162,7 @@ class MinimaxAgent(MultiAgentSearchAgent):
         Returns whether or not the game state is a losing state
         """
         "*** YOUR CODE HERE ***"
-        action = self.result(gameState, 0, self.depth)[1]
-        return action
+        return self.result(gameState, 0, self.depth)[1]
 
     def result(self, state, agentIndex, depth):
         if depth == 0 or state.isLose() or state.isWin():
@@ -198,8 +196,7 @@ class AlphaBetaAgent(MultiAgentSearchAgent):
           Returns the minimax action using self.depth and self.evaluationFunction
         """
         "*** YOUR CODE HERE ***"
-        action = self.result(gameState, 0, self.depth, -1e9, 1e9)[1]
-        return action
+        return self.result(gameState, 0, self.depth, -1e9, 1e9)[1]
 
     def result(self, state, agentIndex, depth, alpha, beta):
         if depth == 0 or state.isLose() or state.isWin():