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We have a game with infinite but countable rounds. We have one machine, that may either break down, or continue operating. For each round the machine operates, it gives cost $-1$ (so profit of $1$). However, it may break down with probability $0.1$ at each round. Our control policy is:

Whenever the machine breaks down pay $c\cdot p^2$ where $c > 0$ is a cost parameter, while $p$ is a variable. The selection of $0 \leq p \leq 1$ gives the probability that repairing the machine will be successful and the machine will operate next round.

So there are two states for the machine: operating, out-of-order (states $O$ and $D$, respectively). My goal is to find out $p$ to minimize my $\alpha$-discounted infinite time horizon cost (we can assume initial state is $O$).

Attempt:

Whenever we are in state $O$, we pay $-1$ cost and go to the $\alpha$-discounted next stage. However, with probability $0.1$ this stage is break-down state, and with $0.9$ probability this is the operating state.

Whenever we are in state $D$, we pay $c\cdot p^2$ and go to the $\alpha$-discounted next stage. This stage will be in state $O$ with probability $p$ and will be $D$ with probability $1-p$.

So the Bellman equations are thus: \begin{align} & V(O) = -1 + \alpha \left[ 0.1 V(D) + 0.9 V(O) \right] \\ & V(D) = c\cdot p^2 + \alpha \left[ (p )V(O) + (1-p) V(D)\right] \end{align} What I do is I re-write the second equation as $V(D) = \text{a function of } V(O)$ and replace this function in the first equation whenever I see $V(D)$. Then, the final expression of $V(O)$ is just a function of $p$: \begin{align} V(O) = \frac{-1 + \alpha - \alpha p + 0.1\alpha cp^2}{(1 - 1.9\alpha + 0.9\alpha^2) + \alpha p - \alpha^2 p} \end{align} I think I should just minimize the above function with respect to $p$. My issues here are:

  1. The second derivative $\geq 0$ is required for convexity (for the usage of FOCs), and the second derivative is massive. I think I also need to constrain $ p \in [0,1]$ so the KKT system is too complicated.
  2. I used a reformulation technique to obtain a convex minimization problem with linear constraint as in here, again it is too complicated and I am afraid if there is some easier way to find the optimal $p$.
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  1. Note that $V(O)$ is simply of the form $\sf Q_1/L_1$ where $\sf Q_1$ is a quadratic and $\sf L_1$ is a linear function of $p$. This can be written as ${\sf{L_2}}+c/\sf{L_1}$ where $\sf L_2$ is also linear in $p$ and $c$ is a constant. Letting ${\sf L_1}:=mp+n$, the second derivative becomes $$V''(O)=c[(mp+n)^{-1}]''=-cm[(mp+n)^{-2}]'=\frac{2cm^2}{(mp+n)^3}$$ which is $\ge0$ if $c(mp+n)\ge0$. Matching the values of $c,m,n$ is straightforward.

Experiment here. It seems $V''(O)\ge0$ whenever $\alpha\ge10/9$ for all $p\in[0,1]$.

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  • $\begingroup$ Thanks for your answer! I am now checking the algebra you suggest. By the way, do you think my optimization over u approach is correct? $\endgroup$ – independentvariable Mar 25 at 20:11
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    $\begingroup$ Unfortunately I lack the expertise to comment on your previous steps on the Bellman equations, but the approach for minimisation seems sensible. Maybe someone can chime in on this. $\endgroup$ – TheSimpliFire Mar 25 at 20:14
  • $\begingroup$ Thanks for that. How can I come with $L_2$? Is there an easy way to apply this split? $\endgroup$ – independentvariable Mar 25 at 20:24
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    $\begingroup$ It's a bit messy, but here you go: \begin{align}\frac{ap^2+bp+c}{dp+e}&=\frac ad\frac{p^2+\frac bap+\frac ca}{p+\frac ed}=\frac ad\frac{p^2+\frac edp+\left(\frac ba-\frac ed\right)p+\frac ca}{p+\frac ed}\\&=\frac ad\left(p+\frac{\left(\frac ba-\frac ed\right)p+\frac ca}{p+\frac ed}\right)=\frac adp+\frac ad\left(\frac ba-\frac ed\right)\frac{p+\frac{c/a}{b/a-e/d}}{p+\frac ed}\\&=\underbrace{\frac adp+\frac{bd-ae}{d^2}}_{\sf L_2}+\frac{c-\frac{e(bd-ae)}{d^2}}{dp+e}\end{align} $\endgroup$ – TheSimpliFire Mar 25 at 20:30
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    $\begingroup$ Then I take it that $x$ would be a scalar, in which case you will need to ensure that $-\alpha (0.1) c\cdot p^2 + 1 - \alpha +\alpha p$ is a perfect square of a linear function... $\endgroup$ – TheSimpliFire Mar 25 at 20:36

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