  Despite the rich theoretical foundation of model-based deep reinforcement
learning (RL) agents, their effectiveness in real-world robotics-applications
is less studied and understood. In this paper, we, therefore, 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 high-dimensional
LiDAR sensors, 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 observation-model choice. Finally,
we provide extensive empirical evidence for the effectiveness of model-based
agents provided with long enough memory horizons in sim2real tasks.
