Understanding state-value and action-value Bellman equations












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In Reinforcement Learning, the Bellman Optimality equations are important for defining optimal policies to be taken by a learning algorithm. The following two equations are commonly cited...



state
...



and



action
...



From a high level I understand how each work, I get that the state-value function returns the optimal policy from going from one state to another and I get that the action-value returns the optimal policy of taking an action from a particular state. What I don't understand is why these equations work out mathematically.



Why is the max function outside of the equation for the state-action function and why is it placed inside for the action-value function? I must be missing some fundamental information about how each equation works. Can someone explain the difference to me?









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    0












    $begingroup$


    In Reinforcement Learning, the Bellman Optimality equations are important for defining optimal policies to be taken by a learning algorithm. The following two equations are commonly cited...



    state
    ...



    and



    action
    ...



    From a high level I understand how each work, I get that the state-value function returns the optimal policy from going from one state to another and I get that the action-value returns the optimal policy of taking an action from a particular state. What I don't understand is why these equations work out mathematically.



    Why is the max function outside of the equation for the state-action function and why is it placed inside for the action-value function? I must be missing some fundamental information about how each equation works. Can someone explain the difference to me?









    share







    New contributor




    Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















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      0








      0





      $begingroup$


      In Reinforcement Learning, the Bellman Optimality equations are important for defining optimal policies to be taken by a learning algorithm. The following two equations are commonly cited...



      state
      ...



      and



      action
      ...



      From a high level I understand how each work, I get that the state-value function returns the optimal policy from going from one state to another and I get that the action-value returns the optimal policy of taking an action from a particular state. What I don't understand is why these equations work out mathematically.



      Why is the max function outside of the equation for the state-action function and why is it placed inside for the action-value function? I must be missing some fundamental information about how each equation works. Can someone explain the difference to me?









      share







      New contributor




      Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      In Reinforcement Learning, the Bellman Optimality equations are important for defining optimal policies to be taken by a learning algorithm. The following two equations are commonly cited...



      state
      ...



      and



      action
      ...



      From a high level I understand how each work, I get that the state-value function returns the optimal policy from going from one state to another and I get that the action-value returns the optimal policy of taking an action from a particular state. What I don't understand is why these equations work out mathematically.



      Why is the max function outside of the equation for the state-action function and why is it placed inside for the action-value function? I must be missing some fundamental information about how each equation works. Can someone explain the difference to me?







      reinforcement-learning markov-process monte-carlo





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