Are there any algorithms to use reinforcement learning to learn optimal policies in partially observable Markov decision process (POMDP) i.e. when the state is not perfectly observed. More specifically, how does one update the belief state using Bayes' rule when the update Q kernel is not known?

  • $\begingroup$ What do you mean by "update Q kernel"? $\endgroup$ – nbro Oct 8 '19 at 0:29
  • $\begingroup$ So a POMDP has a state update kernel Q such that $x_{t+1} \sim Q(\cdot|x_t,a_t)$ and an observation kernel such that the observation $y_t\sim Q^o(\cdot|x_t)$. By update I mean the former, state update kernel $\endgroup$ – Deepanshu Vasal Jan 10 at 22:26
  • $\begingroup$ Can you please link me to a research paper or book (or whatever) that uses the word "kernel"? Maybe this is only a terminology issue, but I don't think that "kernel" is the right word here. $\endgroup$ – nbro Jan 10 at 22:31
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    $\begingroup$ Here is a paper link. In general you can google pomdp transition kernel to get many such results $\endgroup$ – Deepanshu Vasal Jan 12 at 0:28

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