simple reflex agent working

multiAgents.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]).
@@ -14,10 +14,12 @@
 
 from util import manhattanDistance
 from game import Directions
-import random, util
+import random
+import util
 
 from game import Agent
 
+
 class ReflexAgent(Agent):
     """
     A reflex agent chooses an action at each choice point by examining
@@ -28,7 +30,6 @@ class ReflexAgent(Agent):
     headers.
     """
 
-
     def getAction(self, gameState):
         """
         You do not need to change this method, but you're welcome to.
@@ -42,10 +43,13 @@ class ReflexAgent(Agent):
         legalMoves = gameState.getLegalActions()
 
         # Choose one of the best actions
-        scores = [self.evaluationFunction(gameState, action) for action in legalMoves]
+        scores = [self.evaluationFunction(
+            gameState, action) for action in legalMoves]
         bestScore = max(scores)
-        bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore]
+        bestIndices = [index for index in range(
-        chosenIndex = random.choice(bestIndices) # Pick randomly among the best
+            len(scores)) if scores[index] == bestScore]
+        # Pick randomly among the best
+        chosenIndex = random.choice(bestIndices)
 
         "Add more of your code here if you want to"
 
@@ -71,14 +75,32 @@ class ReflexAgent(Agent):
         newPos = successorGameState.getPacmanPosition()
         newFood = successorGameState.getFood()
         newGhostStates = successorGameState.getGhostStates()
-        newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]
+        newScaredTimes = [
+            ghostState.scaredTimer for ghostState in newGhostStates]
         print(newPos)
         print(newFood)
         print(newGhostStates)
         print(newScaredTimes)
 
+        # make is so that if it is closer to a ghost then reduce the score
+        # increase if closer to food
+
+        # only one ghost in classic map
+        ghost_pos = newGhostStates[0].getPosition()
+        nearest_ghost_dist = manhattanDistance(ghost_pos, newPos)
+
+        # nearest food
+        all_food_pos = newFood.asList()
+        score = successorGameState.getScore()
+
+        if len(all_food_pos) and nearest_ghost_dist:
+            nearest_food_dist = min([manhattanDistance(
+                foodPos, newPos) for foodPos in all_food_pos])
+            score = score - (5 / nearest_ghost_dist) + (10 / nearest_food_dist)
+
         "*** YOUR CODE HERE ***"
-        return successorGameState.getScore()
+        return score
+
 
 def scoreEvaluationFunction(currentGameState):
     """
@@ -90,6 +112,7 @@ def scoreEvaluationFunction(currentGameState):
     """
     return currentGameState.getScore()
 
+
 class MultiAgentSearchAgent(Agent):
     """
     This class provides some common elements to all of your
@@ -105,11 +128,12 @@ class MultiAgentSearchAgent(Agent):
     is another abstract class.
     """
 
-    def __init__(self, evalFn = 'scoreEvaluationFunction', depth = '2'):
+    def __init__(self, evalFn='scoreEvaluationFunction', depth='2'):
-        self.index = 0 # Pacman is always agent index 0
+        self.index = 0  # Pacman is always agent index 0
         self.evaluationFunction = util.lookup(evalFn, globals())
         self.depth = int(depth)
 
+
 class MinimaxAgent(MultiAgentSearchAgent):
     """
     Your minimax agent (question 2)
@@ -141,6 +165,7 @@ class MinimaxAgent(MultiAgentSearchAgent):
         "*** YOUR CODE HERE ***"
         util.raiseNotDefined()
 
+
 class AlphaBetaAgent(MultiAgentSearchAgent):
     """
     Your minimax agent with alpha-beta pruning (question 3)
@@ -153,6 +178,7 @@ class AlphaBetaAgent(MultiAgentSearchAgent):
         "*** YOUR CODE HERE ***"
         util.raiseNotDefined()
 
+
 class ExpectimaxAgent(MultiAgentSearchAgent):
     """
       Your expectimax agent (question 4)
@@ -168,6 +194,7 @@ class ExpectimaxAgent(MultiAgentSearchAgent):
         "*** YOUR CODE HERE ***"
         util.raiseNotDefined()
 
+
 def betterEvaluationFunction(currentGameState):
     """
     Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable
@@ -178,5 +205,6 @@ def betterEvaluationFunction(currentGameState):
     "*** YOUR CODE HERE ***"
     util.raiseNotDefined()
 
+
 # Abbreviation
 better = betterEvaluationFunction