Hierarchical Reinforcement Learning for Sensor-Based Navigation

08/30/2021
by   Christopher Gebauer, et al.
0

Robotic systems are nowadays capable of solving complex navigation tasks under real-world conditions. However, their capabilities are intrinsically limited to the imagination of the designer and consequently lack generalizability to initially unconsidered situations. This makes deep reinforcement learning especially interesting, as these algorithms promise a self-learning system only relying on feedback from the environment. Having the system itself search for an optimal solution brings the benefit of great generalization or even constant improvement when life-long learning is addressed. In this paper, we address robot navigation in continuous action space using deep hierarchical reinforcement learning without including the target location in the state representation. Our agent self-assigns internal goals and learns to extract reasonable waypoints to reach the desired target position only based on local sensor data. In our experiments we demonstrate that our hierarchical structure improves the performance of the navigation agent in terms of collected reward and success rate in comparison to a flat structure, while not requiring any global or target information.

READ FULL TEXT

page 1

page 4

research
02/27/2023

Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning

As space becomes more congested, on orbit inspection is an increasingly ...
research
04/27/2018

Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments

Mobile robot navigation in complex and dynamic environments is a challen...
research
03/02/2023

Subgoal-Driven Navigation in Dynamic Environments Using Attention-Based Deep Reinforcement Learning

Collision-free, goal-directed navigation in environments containing unkn...
research
06/04/2020

The growth and form of knowledge networks by kinesthetic curiosity

Throughout life, we might seek a calling, companions, skills, entertainm...
research
09/16/2016

Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

Two less addressed issues of deep reinforcement learning are (1) lack of...
research
02/23/2022

Using Deep Reinforcement Learning with Automatic Curriculum earning for Mapless Navigation in Intralogistics

We propose a deep reinforcement learning approach for solving a mapless ...
research
06/30/2011

Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments

In this paper, we confront the problem of applying reinforcement learnin...

Please sign up or login with your details

Forgot password? Click here to reset