The idea is to optimize the scheduling of Swiss trains, using either operations research (OR) or reinforcement learning (RL).
Currently, they only use OR, however as the scale of the network and the number of trains keeps growing, the hypothesis is that RL may soon provide better approaches - they won't necessarily be globally optimal, but they will be good enough, and faster to re-calculate if something goes wrong.
The competition is currently in-between rounds but it will re-open soon. The idea is to keep it running "forever" as a long-term OR vs RL benchmark.
Disclaimer: I am currently affiliated with AIcrowd.
Comments
If you are interested in this kind of problems, I encourage you to check out the Flatland competition: https://www.aicrowd.com/challenges/flatland-challenge
The idea is to optimize the scheduling of Swiss trains, using either operations research (OR) or reinforcement learning (RL).
Currently, they only use OR, however as the scale of the network and the number of trains keeps growing, the hypothesis is that RL may soon provide better approaches - they won't necessarily be globally optimal, but they will be good enough, and faster to re-calculate if something goes wrong.
The competition is currently in-between rounds but it will re-open soon. The idea is to keep it running "forever" as a long-term OR vs RL benchmark.
Disclaimer: I am currently affiliated with AIcrowd.