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I want to know if there is anything other than neural networks (or Deep NNs) that I can effectively use to perform function approximation? I am asking this w.r.t to the use of approximators in Q learning with large state space.

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  • $\begingroup$ Any parameterisable function does the trick. $\endgroup$ Nov 20 '21 at 0:29
  • $\begingroup$ @DavidIreland Any examples? Actually, I am going to post my scenario specific question after 20 min (as I have to wait before posting another). I will give the link here. I request you to please also take a look on that, if possible. I have been asking small small questions and still stuck. So I decided to ask my scenario specific question finally. $\endgroup$ Nov 20 '21 at 0:33
  • $\begingroup$ @DavidIreland This is the link I said in the above comment. ai.stackexchange.com/questions/32472/… $\endgroup$ Nov 20 '21 at 0:59
  • $\begingroup$ It's a good idea to do a little bit of research before asking a question. Please, take a look at How do I ask a good question?. $\endgroup$
    – nbro
    Nov 20 '21 at 12:38
  • $\begingroup$ it looks like you’re using a graph as the state, in which case I’d recommend looking at graph neural networks. $\endgroup$ Nov 20 '21 at 23:18

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