  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.
