class ApproximateQAgent(PacmanQAgent):
    """
       ApproximateQLearningAgent

       You should only have to overwrite getQValue
       and update.  All other QLearningAgent functions
       should work as is.
    """
    def __init__(self, extractor='SimpleExtractor', **args):
        self.featExtractor = lookup(extractor, globals())()
        PacmanQAgent.__init__(self, **args)
        # self.weights = CustomCounter()
        # self.weights = {'eats-food': 302.5172688564452, 'closest-food': -42.752010792473826, 'bias': 89.99750164854542, '#-of-ghosts-1-step-away': -3503.8012028767084}
        self.weights = {'bias': -277096.33365697827, '#-of-ghosts-1-step-away': -104598.07945904322, 'eats-food': 399786.0660682465, 'closest-food': -365.4721024438045}
    def getWeights(self):
        return self.weights

    def getQValue(self, state, action):
        """
          Should return Q(state,action) = w * featureVector
          where * is the dotProduct operator
        """
        "*** YOUR CODE HERE ***"
        features = self.featExtractor.getFeatures(state, action)
        result = 0
        for feature in features:
            result += self.weights[feature] * features[feature]
        return result

    def update(self, state, action, nextState, reward):
        """
           Should update your weights based on transition
        """
        "*** YOUR CODE HERE ***"
        features = self.featExtractor.getFeatures(state, action)
        correction = reward + self.discount * self.getValue(nextState) - self.getQValue(state, action)
        for feature in features:
            self.weights[feature] += self.alpha * correction * features[feature]

    def final(self, state):
        "Called at the end of each game."
        # call the super-class final method
        PacmanQAgent.final(self, state)

        # did we finish training?
        if self.episodesSoFar == self.numTraining:
            # you might want to print your weights here for debugging
            "*** YOUR CODE HERE ***"
            print(self.weights)
            pass