We consider the challenge of automated steering angle prediction for self
driving cars using egocentric road images. In this work, we explore the use of
feudal networks, used in hierarchical reinforcement learning (HRL), to devise a
vehicle agent to predict steering angles from first person, dash-cam images of
the Udacity driving dataset. Our method, Feudal Steering, is inspired by recent
work in HRL consisting of a manager network and a worker network that operate
on different temporal scales and have different goals. The manager works at a
temporal scale that is relatively coarse compared to the worker and has a
higher level, task-oriented goal space. Using feudal learning to divide the
task into manager and worker sub-networks provides more accurate and robust
prediction. Temporal abstraction in driving allows more complex primitives than
the steering angle at a single time instance. Composite actions comprise a
subroutine or skill that can be re-used throughout the driving sequence. The
associated subroutine id is the manager network's goal, so that the manager
seeks to succeed at the high level task (e.g. a sharp right turn, a slight
right turn, moving straight in traffic, or moving straight unencumbered by
traffic). The steering angle at a particular time instance is the worker
network output which is regulated by the manager's high level task. We
demonstrate state-of-the art steering angle prediction results on the Udacity
dataset.

本论文提供了一种基于封建网络和分层强化学习的车辆代理预测从车辆驾驶数据集中的第一视角实时图像进行自动驾驶方向盘角度预测的新方法，演示了在 Udacity 数据集上最先进的预测结果。

封建式转向：针对转向角预测的层次学习

Feudal Steering: Hierarchical Learning for Steering Angle Prediction

We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical
reinforcement learning. Our approach is inspired by the feudal reinforcement
learning proposal of Dayan and Hinton, and gains power and efficacy by
decoupling end-to-end learning across multiple levels -- allowing it to utilise
different resolutions of time. Our framework employs a Manager module and a
Worker module. The Manager operates at a lower temporal resolution and sets
abstract goals which are conveyed to and enacted by the Worker. The Worker
generates primitive actions at every tick of the environment. The decoupled
structure of FuN conveys several benefits -- in addition to facilitating very
long timescale credit assignment it also encourages the emergence of
sub-policies associated with different goals set by the Manager. These
properties allow FuN to dramatically outperform a strong baseline agent on
tasks that involve long-term credit assignment or memorisation. We demonstrate
the performance of our proposed system on a range of tasks from the ATARI suite
and also from a 3D DeepMind Lab environment.

介绍了一种新的分层强化学习方法，即 FeUdal Networks，该方法采用 “管理者 - 工作者” 框架，在不同的时间分辨率上进行端到端的学习，以完成由管理者设定的抽象目标，并且旨在解决长时间跨度的信用分配问题，并在 ATARI 和 DeepMind Lab 中的任务上取得显著优势。