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)