My while loop iterates 2-5 times but doesn't get up to the 100 iterations I want it to. I need at least 100 iterations to extract enough data from my simulation. Here is my code, appreciate the help.

class Robotreacher:
def __init__(self):
    rospy.init_node('robot_control_node', anonymous=True)
    self.talker = rospy.Publisher('/rrbot/joint1_position_controller/command', Float64, queue_size=10)
    rospy.Subscriber('/rrbot/joint_states', JointState, self.joint_callback)
    self.force = None
    self.joint1 = 0
    self.target1 = random.randint(10, 62)/10
    self.start_time = time.time()
    self.rate = rospy.Rate(10)
    self.reward = 0
    print("the target is", self.target1)
    print("the reward is", self.reward)



    while self.observation_check() is False:
        self.give_force()
        self.rate.sleep()


def give_force(self):
    self.force =  random.randrange(-12, 12, 1)  # Changes force every time, right before publishing
    self.talker.publish(self.force)
    #print("the force is ", self.force)
    return self.force            

def joint_callback(self, data):
    #print(data)
    self.joint1 = abs(data.position[0])%(2*math.pi)


def observation_check(self):
    target2 = self.target1 - 0.2
    if target2 < self.joint1 < self.target1:
        done = True
    else:
        done = False
    return done

def what_is_reward(self):
    #reward = None
    if self.observation_check() is True:
        self.reward = (time.time() - self.start_time)

    return self.reward
    print("the reward is", self.reward)

def get_joint(self):
    return self.joint1
    #print(self.joint1)



def robotcontrolloop2():
    num_trials = 10
    min_score = 0
    trainingZ, trainingP = [], []
    scores = []
    for _ in range(num_trials):
        i=0
        training_samplex = []
        r = Robotreacher()
        #w = r.give_force()
        #print("this is force", w)
        training_samplez = []
        training_samplep = []
        score = 0

        while i < 100:
            z = r.give_force()
            one_hot_action = np.zeros(25)
            one_hot_action[z] = 1
            #print(one_hot_action)
            #print("the force is",z)
            time.sleep(0.7)
            p = r.get_joint()
            #print("the position is", p)
            training_samplez.append(one_hot_action)
            #self.rate.sleep()
            training_samplep.append(p)
            score += r.what_is_reward()
            i += 1
        #print(training_z)
        #print(training_p)
        print(score)
        if score > min_score:
            scores.append(score)
            trainingZ += training_samplez
            trainingP += training_samplep

    trainingZ, trainingP = np.array(trainingZ), np.array(trainingP)
    print("Average: {}".format(np.mean(scores)))
    #print("Median: {}".format(np.median(scores)))
    return trainingZ, trainingP
robotcontrolloop2()