  World models learn behaviors in a latent imagination space to enhance the
sample-efficiency of deep reinforcement learning (RL) algorithms. While
learning world models for high-dimensional observations (e.g., pixel inputs)
has become practicable on standard RL benchmarks and some games, their
effectiveness in real-world robotics applications has not been explored. In
this paper, we investigate how such agents generalize to real-world autonomous
vehicle control tasks, where advanced model-free deep RL algorithms fail. In
particular, we set up a series of time-lap tasks for an F1TENTH racing robot,
equipped with a high-dimensional LiDAR sensor, on a set of test tracks with a
gradual increase in their complexity. In this continuous-control setting, we
show that model-based agents capable of learning in imagination substantially
outperform model-free agents with respect to performance, sample efficiency,
successful task completion, and generalization. Moreover, we show that the
generalization ability of model-based agents strongly depends on the choice of
their observation model. We provide extensive empirical evidence for the
effectiveness of world models provided with long enough memory horizons in
sim2real tasks.
