learn-to-race/l2r
Open-source reinforcement learning environment for autonomous racing — featured as a conference paper at ICCV 2021 and as the official challenge tracks at both SL4AD@ICML2022 and AI4AD@IJCAI2022. These are the L2R core libraries.
PythonGPL-2.0
Stargazers
- AdrianOrensteinEdmonton, CA
- araffin@DLR-RM
- axelbrTU Wien
- bektaskemalNode Robotics GmbH
- bingqingchen
- caizicheng
- David-ChennNone
- dtokiVancouver, Canada
- fernandolis10
- friendship1
- GilgameshDNVIDIA
- haomingyCarnegie Mellon University
- irosyadiUniversitas Jenderal Soedirman
- jinyeom@microsoft
- JohannesBetzTechnical University of Munich
- JoovvhanKRAFTON
- jwdiniusLos Angeles, CA
- kchian
- koulSan Francisco
- kubic71Prague
- lingjiekongGoogle DeepMind
- loser-whyShanghai
- MatthewHoweAustralian Institute for Machine Learning, The University of Adelaide
- mcemilgİstanbul
- MicheleDamian
- sheelabhadraTexas A&M University
- shravb
- tanay-gangey
- vijpandaturtleAmrita Vishwa Vidyapeetham
- vs-sat-dev
- weirayaoSalesforce
- XDynames
- Xyla030Weill Cornell Medicine
- yuwei-wuUniversity of Pennsylvania
- YYan99Sun Yat-Sen University
- zmlshiwo