---
title: "QMDP"
source: https://www.jemoka.com/posts/kbhqmdp/
---

One alpha vector per action:
\begin{equation} \alpha^{(k+1)}_{a}(s) = R(s,a) + \gamma \sum_{s&rsquo;}^{}T(s&rsquo;|s,a) \max_{a&rsquo;} \alpha^{(k)}_{a&rsquo;} (s&rsquo;) \end{equation}
This is going to give you a set of alpha vectors, one corresponding to each action.
time complexity: \(O(|S|^{2}|A|^{2})\)
you will note we don&rsquo;t ever actually use anything partially-observable in this. Once we get the alpha vector, we need to use one-step lookahead in POMDP (which does use transitions) to actually turn this alpha vector into a policy, which then does create you
We can deal with continuous state space by using some estimation of the value function (instead of alpha-vectors, we will just use a value-function estimate like q learning)
