Feudal Steering: Hierarchical Learning for Steering Angle Prediction

TL;DR

This paper introduces Feudal Steering, a hierarchical learning approach using feudal networks to improve steering angle prediction in self-driving cars by leveraging temporal abstraction and subroutine reuse.

Contribution

The paper proposes a novel hierarchical reinforcement learning framework for steering prediction, dividing the task into manager and worker networks for better accuracy and robustness.

Findings

Achieved state-of-the-art steering prediction accuracy on Udacity dataset.

Demonstrated improved robustness through hierarchical task decomposition.

Showed that temporal abstraction enhances complex primitive learning.

Abstract

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.