via automation

Using dynamic programming, as introduced previously, we eventually found the shortest route to the summit. We are finally at the top of the mountain! Naturally, many of us will directly reach for our phones. Perhaps to take a selfie with friends, to construct a panorama of neighbouring mountain tops, or maybe to record a video of some alpine birds effortlessly gliding around.

We’ve made it up from the valley, thanks to our gondola and optimal control. Finally, we will plan our path to the summit! In this post we will do precisely that, but in a systematic way. It turns out that this connects to reinforcement learning.

After going up in the gondola and appreciating how smooth the ride was (in part, thanks to automatic control!), lets quickly grab a coffee before we ascend the mountain.

In this second stage we leave the valley and move a bit higher up. Of course, we could walk or climb, but let us enjoy a remarkable piece of engineering and catch a gondola (cable car).

We start our hike in a typical Swiss valley: tidy green fields, some cozy chalets, but most importantly, a herd of curious cows that welcomes us.

In our new series of explainers, let Wouter Jongeneel walk you through both Switzerland and automatic control.