v1.1.2

This is a small release with two fixes for very specific issues:

• The Q-learning algorithm is still a proof-of-concept in this application, because Value Iteration (which is the default algorithm) is faster and gives optimal results. Still, Q-learning suffered from an old piece of code that tried helping to "guide" the algorithm into quicker more optimal action selection. In ongoing tests, this part of code made Q-learning exceptionally slow in comparison, so we removed it. Planning algorithms in combination with Q-learning should be a better alternative. However, we have not yet dived deep into this yet, since Value Iteration works so well in our use-case. So Q-learning should be faster now, although it is still a proof-of-concept rather than the recommended algorithm to choose here.
• When exporting third-party network data as artificial OSM data file, we did not export any tags with the ways. Although tags can be later added artificially with osmosis, it is easier to just modify the tags with osmosis, osmconvert, or osmium. However, modification only works when there are tags, so we use the temporary highway=road tag (that is explicitely reserved for this purpose) now. This should make later working with artificial exported OSM files easier.
• We updated the date library to 3.0.4 and protozero to 1.8.1
• Some build system adaptions were made for working with address sanitizers and perf in Docker environment

Full Changelog: v1.1.1...v1.1.2