路由地址，应该填啥，怎么去看呢。

这个是若依框架，然后自己新加的模块，路由地址怎么填呢，有懂得人可以看一看，多谢

填写VUE的路由

主要就是标识是那个路由，如图所示

• 这个问题的回答你可以参考下: https://ask.csdn.net/questions/7618442
• 你也可以参考下这篇文章：并行多图，顺序多图，选项卡地图，时间线轮播图的动态图片的绘制
• 除此之外, 这篇博客: 机器学习之强化学习---《机器学习-周志华》学习笔记中的 7.对于目标驱动的强化学习任务，目标是到达某一状态，例如机器人走到预定的位置，假设机器人只能在一维空间运动，即只能向左或者向右运动，机器人起始位置在最左侧，预定的位置在最右侧，试为这样的任务设置奖赏规则，并编程实现。 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  

• 
  import numpy as np
  import pandas as pd
  import time
  
  np.random.seed(2)  # reproducible
  
  
  N_STATES = 6   # the length of the 1 dimensional world
  ACTIONS = ['left', 'right']     # available actions
  EPSILON = 0.9   # greedy police
  ALPHA = 0.1     # learning rate
  GAMMA = 0.9    # discount factor
  MAX_EPISODES = 13   # maximum episodes
  FRESH_TIME = 0.3    # fresh time for one move
  
  
  def build_q_table(n_states, actions):
      table = pd.DataFrame(
          np.zeros((n_states, len(actions))),     # q_table initial values
          columns=actions,    # actions's name
      )
      # print(table)    # show table
      return table
  
  
  def choose_action(state, q_table):
      # This is how to choose an action
      state_actions = q_table.iloc[state, :]
      if (np.random.uniform() > EPSILON) or ((state_actions == 0).all()):  # act non-greedy or state-action have no value
          action_name = np.random.choice(ACTIONS)
      else:   # act greedy
          action_name = state_actions.idxmax()    # replace argmax to idxmax as argmax means a different function in newer version of pandas
      return action_name
  
  
  def get_env_feedback(S, A):
      # This is how agent will interact with the environment
      if A == 'right':    # move right
          if S == N_STATES - 2:   # terminate
              S_ = 'terminal'
              R = 1
          else:
              S_ = S + 1
              R = 0
      else:   # move left
          R = 0
          if S == 0:
              S_ = S  # reach the wall
          else:
              S_ = S - 1
      return S_, R
  
  
  def update_env(S, episode, step_counter):
      # This is how environment be updated
      env_list = ['-']*(N_STATES-1) + ['T']   # '---------T' our environment
      if S == 'terminal':
          interaction = 'Episode %s: total_steps = %s' % (episode+1, step_counter)
          print('\r{}'.format(interaction), end='')
          time.sleep(2)
          print('\r                                ', end='')
      else:
          env_list[S] = 'o'
          interaction = ''.join(env_list)
          print('\r{}'.format(interaction), end='')
          time.sleep(FRESH_TIME)
  
  
  def rl():
      # main part of RL loop
      q_table = build_q_table(N_STATES, ACTIONS)
      for episode in range(MAX_EPISODES):
          step_counter = 0
          S = 0
          is_terminated = False
          update_env(S, episode, step_counter)
          while not is_terminated:
  
              A = choose_action(S, q_table)
              S_, R = get_env_feedback(S, A)  # take action & get next state and reward
              q_predict = q_table.loc[S, A]
              if S_ != 'terminal':
                  q_target = R + GAMMA * q_table.iloc[S_, :].max()   # next state is not terminal
              else:
                  q_target = R     # next state is terminal
                  is_terminated = True    # terminate this episode
  
              q_table.loc[S, A] += ALPHA * (q_target - q_predict)  # update
              S = S_  # move to next state
  
              update_env(S, episode, step_counter+1)
              step_counter += 1
      return q_table
  
  
  if __name__ == "__main__":
      q_table = rl()
      print('\r\nQ-table:\n')
      print(q_table)