  Autonomous car racing is a challenging task in the robotic control area.
Traditional modular methods require accurate mapping, localization and
planning, which makes them computationally inefficient and sensitive to
environmental changes. Recently, deep-learning-based end-to-end systems have
shown promising results for autonomous driving/racing. However, they are
commonly implemented by supervised imitation learning (IL), which suffers from
the distribution mismatch problem, or by reinforcement learning (RL), which
requires a huge amount of risky interaction data. In this work, we present a
general deep imitative reinforcement learning approach (DIRL), which
successfully achieves agile autonomous racing using visual inputs. The driving
knowledge is acquired from both IL and model-based RL, where the agent can
learn from human teachers as well as perform self-improvement by safely
interacting with an offline world model. We validate our algorithm both in a
high-fidelity driving simulation and on a real-world 1/20-scale RC-car with
limited onboard computation. The evaluation results demonstrate that our method
outperforms previous IL and RL methods in terms of sample efficiency and task
performance. Demonstration videos are available at
https://caipeide.github.io/autorace-dirl/
