L2RACE
Learning to race (L2RACE)
Learning to control dynamical systems is a hot topic. We will explore it in a simple virtual race track where contestants duke it out using their software agent. The cars will have unknown dynamics on a known track (which has unknown traction characteristics). Contestants get full state information (eye of god, so no need for fancy perception/SLAM) and use it to learn how to control the cars to win a time trial or race. Data collection will be real-time (no transfer learning tricks), but can use E2E human data. This will be a pure python ML contest of the best control learning algorithm.
By contrast with the excellent F1TENTH challenge, L2RACE will be much simpler and aimed purely at efficiently learning a good controller from the minimum amount of data.
See our l2race github repo for more information and latest code.
Organizing Team
Marcin Paluch (ETH Zurich)
Antonio Rios (U Seville)
Chang Gao (UZH-ETH Zurich)
Tobi Delbruck (UZH-ETH Zurich)