Body: <p>I am trying to construct an RL algorithm for managing a fleet of vehicles to maximize profit. As far as I know, the Sequential Decision Process can be decomposed into the following pic:</p>
  <p><a href="https://i.sstatic.net/DBOMx.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/DBOMx.png" alt="enter image description here" /></a></p>
  <p>My current method is based on value function approximation of post-decision States <span class="math-container">$S_t^x$</span>. But without the technique of Target Network and Experience Replay of DQN, my model is easily trapped at the local minima and can not take advantage of the earlier exploration steps just like DQN. Are there any deep RL algorithms that are based on post-decision states?</p>
  <p>Here I enclose one paper for managing robots using the post-decision states: <a href="https://ris.utwente.nl/ws/portalfiles/portal/286067833/Akkerman2022comparitive.pdf" rel="nofollow noreferrer">A Comparative Analysis of Neural Networks in Anticipatory Transportation Planning</a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-07-27T00:36:06.933Z
Comments.Text: It might be a good idea to briefly describe these "post-decision states" and the technique you're currently using to solve your problem. Is it something known in the literature or something you came up with? Moreover, it might also be a good idea to provide more info about your specific problem. "managing a fleet of vehicles to maximize profit." is not very clear (at least to me).
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-07-28T23:36:37.470Z
Comments.Text: Hello @nbro, I enclosed a paper using post-decision states.
Comments.UserDisplayName: PokeLu
Tags: <reinforcement-learning><deep-rl><dqn>
Title: Is there any Deep RL method that is based on value function approximation of Post-decision States
UserDisplayName: PokeLu
ThreadText: # Is there any Deep RL method that is based on value function approximation of Post-decision States
  
  (Asked by: username_0 on 2023-07-23)
  
  I am trying to construct an RL algorithm for managing a fleet of vehicles to maximize profit. As far as I know, the Sequential Decision Process can be decomposed into the following pic:
  
  [![enter image description here](https://i.sstatic.net/DBOMx.png)](https://i.sstatic.net/DBOMx.png)
  
  My current method is based on value function approximation of post-decision States $S\_t^x$. But without the technique of Target Network and Experience Replay of DQN, my model is easily trapped at the local minima and can not take advantage of the earlier exploration steps just like DQN. Are there any deep RL algorithms that are based on post-decision states?
  
  Here I enclose one paper for managing robots using the post-decision states: [A Comparative Analysis of Neural Networks in Anticipatory Transportation Planning](https://ris.utwente.nl/ws/portalfiles/portal/286067833/Akkerman2022comparitive.pdf)
  
  **Tags:** reinforcement-learning, deep-rl, dqn
  > **Comments:**
  > **username_1**: It might be a good idea to briefly describe these "post-decision states" and the technique you're currently using to solve your problem. Is it something known in the literature or something you came up with? Moreover, it might also be a good idea to provide more info about your specific problem. "managing a fleet of vehicles to maximize profit." is not very clear (at least to me).  
  > **username_0**: Hello @nbro, I enclosed a paper using post-decision states.  
  
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  ## 0 answers
  
  