add faq entries (#165)

* migrates the mlr3 wiki FAQ
* add FAQ on how to use an old mlr3 model

mlr-org/faq.qmd

@@ -16,6 +16,9 @@ title: Frequently Asked Questions
 * [Preprocessing factor levels](#factor-levels)
 * [Memory Problems](#memory-problems)
 * [How can I suppress logging output of learners on the R console](#suppress-output)
+* [A learner trained with an old mlr3 version does not work anymore](#old-model-broken)
+* [Caching of knitr/rmarkdown chunks does not work with mlr3](#caching-knitr)
+* [How to keep all mlr3 packages up-to-date?](#update-packages)
 
 ## Why is there only the rpart learner? {#learner}
 
@@ -102,3 +105,53 @@ mylearner$encapsulate = c(train = "evaluate", predict = "evaluate")
 mylearner$train(mytask)
 ```
 
+## A learner trained with an old mlr3 version does not work anymore {#old-model-broken}
+
+It is possible that a saved `Learner` that was trained with an old `mlr3` version does not work with a different version of `mlr3`.
+In general, we recommend saving the computational environment using a tool like [renv](https://github.com/rstudio/renv) so this can later be restored and avoiding such situations alltogether.
+If this is not an option, a possible workaround is to construct the same learner in the currently used `mlr3` version and manually set its `$state` to the one of the saved learner.
+This is illustrated below:
+
+1. Using an old `mlr3` version:
+
+   ```{r, eval = FALSE}
+   learner = lrn("classif.rpart")
+   learner$train(tsk("iris"))
+   saveRDS(learner, "learner.rds")
+   ```
+
+1. With a subsequent `mlr3` version:
+
+   ```{r, eval = FALSE}
+   learner = lrn("classif.rpart")
+   learner_old = readRDS("learner.rds")
+   learner$state = learner_old$state
+   ```
+
+Note that this is **risky** and **not** guaranteed to work because of various reasons:
+* You might have now loaded a different version of the learner library (in this case the `rpart` pacakge).
+* The internals (such as the structure of the internal `$state`) might have changed between the versions.
+
+Therefore, be careful when attempting this solution and double-check that the learner behaves sensibly.
+
+## Caching of knitr/rmarkdown chunks does not work with mlr3 {#caching-knitr}
+
+{knitr} per default uses R's lazy-load database to store the results of individual chunks.
+The lazy-load database is an internal feature of R, and has issues handling active bindings ([https://github.com/r-lib/R6/issues/152](https://github.com/r-lib/R6/issues/152)).
+Fortunately, it is possible to disable lazy-loading by setting the chunk option `cache.lazy` to `FALSE`:
+
+```{r, eval = FALSE}
+knitr::opts_chunk$set(cache = TRUE, cache.lazy = FALSE)
+```
+
+## How to keep all mlr3 packages up-to-date? {#update-packages}
+
+Either run R's `update.packages()` to update all installed packages, or run
+
+```r
+devtools::update_packages("mlr3verse", dependencies = TRUE)
+```
+
+to update only packages from the mlr3verse.
+Note that this also updates recursive dependencies not listed as a direct import.
+