One alpha vector per action:

\begin{equation} \alpha^{(k+1)}_{a}(s) = R(s,a) + \gamma \sum_{s’}^{}T(s’|s,a) \max_{a’} \alpha^{(k)}_{a’} (s’) \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’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

system does not work as well for one type/group of people compared to another

training data really does matter: it may make generalized predictions based on a majority/minority class.

Because IID characteristic of input data, the majority will be over represented

Here’s a PSPACE-COMPLETE language:

\begin{equation} \forall x_1, \exists x_2, \forall x_3 \dots \dots \phi\qty(x_1, x_2, \dots, x_{n}) = 1 \end{equation}

Plot each value of \(\alpha=\) (i.e. probability mass this much) between qmodel vs. qtrue; a best fit would be diagonal