text: # valueIterationAgents.py
  # -----------------------
  # Licensing Information:  You are free to use or extend these projects for
  # 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
  # (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
  # Student side autograding was added by Brad Miller, Nick Hay, and
  # Pieter Abbeel (pabbeel@cs.berkeley.edu).
  
  
  import mdp, util
  import operator #Necessário para pegar o maior valor no dicionário
  
  from learningAgents import ValueEstimationAgent
  
  class ValueIterationAgent(ValueEstimationAgent):
      """
          * Please read learningAgents.py before reading this.*
  
          A ValueIterationAgent takes a Markov decision process
          (see mdp.py) on initialization and runs value iteration
          for a given number of iterations using the supplied
          discount factor.
      """
      def __init__(self, mdp, discount = 0.9, iterations = 100):
          """
            Your value iteration agent should take an mdp on
            construction, run the indicated number of iterations
            and then act according to the resulting policy.
  
            Some useful mdp methods you will use:
                mdp.getStates()
                mdp.getPossibleActions(state)
                mdp.getTransitionStatesAndProbs(state, action)
                mdp.getReward(state, action, nextState)
                mdp.isTerminal(state)
          """
          self.mdp = mdp
          self.discount = discount
          self.iterations = iterations
          self.values = util.Counter() # A Counter is a dict with default 0
  
          # Write value iteration code here
          "*** YOUR CODE HERE ***"
          
          for _ in range(self.iterations):
              Vt = self.values.copy()
              for state in self.mdp.getStates():
                  #print('Estado: ', state)
                  maiorValor = float('-inf')                
                  melhorAcao = self.computeActionFromValues(state)
                  #print('Melhor acao: ', melhorAcao)
                  if melhorAcao != None:
                      melhorValor = self.computeQValueFromValues(state, melhorAcao)
                      if melhorValor > maiorValor:
                          #print('Melhor valor: ', melhorValor)
                          maiorValor = melhorValor
                          Vt[state] = melhorValor
  
              self.values = Vt
  
  
      def getValue(self, state):
          """
            Return the value of the state (computed in __init__).
          """
          return self.values[state]
  
  
      def computeQValueFromValues(self, state, action):
          """
            Compute the Q-value of action in state from the
            value function stored in self.values.
          """
          "*** YOUR CODE HERE ***"
          valorDaAcao = 0
          for next_state, prob in self.mdp.getTransitionStatesAndProbs(state, action):            
              #if self.mdp.isTerminal(next_state):
                  #continue
              reward = self.mdp.getReward(state, action, next_state)
              valorDaAcao += (prob * (reward + self.discount * self.values[next_state]))
          
          return valorDaAcao
  
      def computeActionFromValues(self, state):
          """
            The policy is the best action in the given state
            according to the values currently stored in self.values.
  
            You may break ties any way you see fit.  Note that if
            there are no legal actions, which is the case at the
            terminal state, you should return None.
          """
          "*** YOUR CODE HERE ***"
          best_action = {} #Dicionario (acao, valor)
  
          if self.mdp.isTerminal(state):
              return None
  
          for action in self.mdp.getPossibleActions(state):
              valorDaAcao = self.computeQValueFromValues(state, action)
              best_action[action] = valorDaAcao #Vai adicionado os valores
  
  
          melhorAcao = max(best_action.items(), key=operator.itemgetter(1))[0]
          return melhorAcao
  
          #util.raiseNotDefined()
  
      def getPolicy(self, state):
          return self.computeActionFromValues(state)
  
      def getAction(self, state):
          "Returns the policy at the state (no exploration)."
          return self.computeActionFromValues(state)
  
      def getQValue(self, state, action):
          return self.computeQValueFromValues(state, action)
  