Finish problem 2.

p2_multiagent/multiAgents.py

@@ -223,8 +223,27 @@ class ExpectimaxAgent(MultiAgentSearchAgent):
           All ghosts should be modeled as choosing uniformly at random from their
           legal moves.
         """
-        "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+        numAgents = gameState.getNumAgents()
+        totalDepth = self.depth * numAgents
+
+        def value(depth, state):
+            agentIndex = depth % numAgents
+            actions = state.getLegalActions(agentIndex)
+            if not actions or depth == totalDepth:
+                return (self.evaluationFunction(state), "terminal")
+            successorStates = [state.generateSuccessor(agentIndex, action) for action in actions]
+            successorValueActionPairs = [(value(depth + 1, state)[0], action)
+                                          for action, state in zip(actions, successorStates)]
+            # Pacman (agentIndex=0) maximizes, ghosts minimize.
+            if agentIndex == 0:
+                return max(successorValueActionPairs)
+            else:
+                values = [va[0] for va in successorValueActionPairs]
+                average = sum(values) / float(len(values))
+                return (average, "expected")
+
+        # [0] is the best value, [1] is the best action
+        return value(0, gameState)[1]
 
 
 def betterEvaluationFunction(currentGameState):
@@ -234,9 +253,51 @@ def betterEvaluationFunction(currentGameState):
 
       DESCRIPTION: <write something here so we know what you did>
     """
-    "*** YOUR CODE HERE ***"
-    util.raiseNotDefined()
 
+    from searchAgents import mazeDistance
+
+    state = currentGameState
+    pos = state.getPacmanPosition()
+
+    # foodDists = [mazeDistance(pos, foodPos, state)
+                #  for foodPos in state.getFood().asList()]
+    scaredTimeScore = 0
+    scaredTimes = [ghostSt.scaredTimer for ghostSt in state.getGhostStates()]
+    if scaredTimes:
+        scaredTimeScore = min(scaredTimes)
+
+    ghostDists = []
+    for ghostState in state.getGhostStates():
+        x, y = ghostState.getPosition()
+        ghostPos = (int(x), int(y))
+        distance = mazeDistance(pos, ghostPos, state)
+        ghostDists.append(distance)
+    if ghostDists:
+        try:
+            ghostScore = 1. / min(ghostDists)
+        except ZeroDivisionError:
+            ghostScore = 100
+
+    foodDists = [manhattanDistance(pos, foodPos)
+                 for foodPos in state.getFood().asList()]
+    foodScore = 0
+    if foodDists:
+        foodScore = 1. / min(foodDists)
+    
+    gameScore = state.getScore()
+
+    weightGhost = -0.01
+    weightFood = 0.5
+    weightScore = 0.2
+    weightScaredTime = 0.01
+
+    score = ghostScore * weightGhost + \
+            foodScore * weightFood + \
+            gameScore * weightScore + \
+            scaredTimeScore * weightScaredTime
+    # print(state)
+    # print(score, ghostScore, foodScore, gameScore, scaredTimeScore)
+    return score
 
 # Abbreviation
 better = betterEvaluationFunction