def evaluatePolicy(self, policy_matrix, gamma):
        temp = 0.2
        #while(temp>0.1):
        #delta=0
        for x in range(1):
            temp = 0
            for i in range(ROW_NUM):
              #  print("here1")
                for j in range(COL_NUM):
               #     print("here2")
                    state = self.state_space.state2Darray[i][j]
                    #value = self.return_value(state)
                    value = self.value_matrix[i][j]
                    a = self.policy_matrix[state.row][state.col]
                    self.value_matrix[i][j] = self.getSum(state, a, gamma)
                    #abs_value = abs(value-self.value_matrix[i][j])

                    #temp = max(temp, abs(value-self.value_matrix[i][j]))

    def getSum(self, s, a, gamma):
        sum1 = 0
        for i in range(ROW_NUM):
            #print("here3")
            for j in range (COL_NUM):
             #   print("here4")
                s_prime = self.state_space.state2Darray[i][j]
                New_a = robotAction(a)
                t_fn = to_Next_State_Prob(New_a, s, self.error, s_prime)
                #v_fn = self.return_value(s_prime)
                v_fn = self.value_matrix[i][j]
                #print(v_fn)
                reward = self.state_space.getStateReward(i,j)
                #print(reward)
                sum1 = sum1 + t_fn*(reward + gamma*v_fn)
                if (sum1 > 100):
                        sum1 = sum1/100
        print(sum1)
        return sum1