Deriving robust control policies for realistic urban navigation scenarios is
not a trivial task. In an end-to-end approach, these policies must map
high-dimensional images from the vehicle's cameras to low-level actions such as
steering and throttle. While pure Reinforcement Learning (RL) approaches are
based exclusively on rewards,Generative Adversarial Imitation Learning (GAIL)
agents learn from expert demonstrations while interacting with the environment,
which favors GAIL on tasks for which a reward signal is difficult to derive. In
this work, the hGAIL architecture was proposed to solve the autonomous
navigation of a vehicle in an end-to-end approach, mapping sensory perceptions
directly to low-level actions, while simultaneously learning mid-level input
representations of the agent's environment. The proposed hGAIL consists of an
hierarchical Adversarial Imitation Learning architecture composed of two main
modules: the GAN (Generative Adversarial Nets) which generates the Bird's-Eye
View (BEV) representation mainly from the images of three frontal cameras of
the vehicle, and the GAIL which learns to control the vehicle based mainly on
the BEV predictions from the GAN as input.Our experiments have shown that GAIL
exclusively from cameras (without BEV) fails to even learn the task, while
hGAIL, after training, was able to autonomously navigate successfully in all
intersections of the city.

本文提出了一种基于层次化对抗性模仿学习的架构 hGAIL，该架构可以将车辆的传感器感知直接映射到低层动作，并同时学习到中层的输入表征，实现了无人驾驶车辆的自主导航。