Papers › Learning to drive from a world on rails
Learning to drive from a world on rails
Dian Chen, Vladlen Koltun, Philipp Krähenbühl
We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving trajectory. To support learning from pre-recorded logs, we assume that the world is on rails, meaning neither the agent nor its actions influence the environment. This assumption greatly simplifies the learning problem, factorizing the dynamics into a nonreactive world model and a low-dimensional and compact forward model of the ego-vehicle. Our approach computes action-values for each training trajectory using a tabular dynamic-programming evaluation of the Bellman equations; these action-values in turn supervise the final vision-based driving policy. Despite the world-on-rails assumption, the final driving policy acts well in a dynamic and reactive world. At the time of writing, our method ranks first on the CARLA leaderboard, attaining a 25% higher driving score while using 40 times less data. Our method is also an order of magnitude more sample-efficient than state-of-the-art model-free reinforcement learning techniques on navigational tasks in the ProcGen benchmark.
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Code
Syntology Ran 2 of 6 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
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Code Syntology ran Syntology
6 samples harvested; 2 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Autonomous Driving | CARLA Leaderboard | World on Rails | Driving Score | 31.37 | #13 of 18 | Archive leaderboard | report |
| Autonomous Driving | CARLA Leaderboard | World on Rails | Infraction penalty | 0.56 | #13 of 18 | Archive leaderboard | report |
| Autonomous Driving | CARLA Leaderboard | World on Rails | Route Completion | 57.65 | #13 of 18 | Archive leaderboard | report |
| CARLA longest6 | CARLA | World on Rails (WOR) | Driving Score | 21 | #21 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | World on Rails (WOR) | Infraction Score | 0.56 | #21 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | World on Rails (WOR) | Route Completion | 48 | #21 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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