Toward Self-Driving Bicycles Using State-of-the-Art Deep Reinforcement Learning Algorithms

In this paper, we propose a controller for a bicycle using the DDPG (Deep Deterministic Policy Gradient) algorithm, which is a state-of-the-art deep reinforcement learning algorithm. We use a reward function and a deep neural network to build the controller. By using the proposed controller, a bicyc...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Symmetry (Basel) Jg. 11; H. 2; S. 290
Hauptverfasser: Choi, SeungYoon, Le, Tuyen P., Nguyen, Quang D., Layek, Md Abu, Lee, SeungGwan, Chung, TaeChoong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.02.2019
Schlagworte:
Algorithms
Artificial neural networks
Bicycles
Control theory
Controllers
Deep learning
Learning
Machine learning
Neural networks
Optimization
Performance evaluation
Tires
Velocity
ISSN:2073-8994, 2073-8994
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, we propose a controller for a bicycle using the DDPG (Deep Deterministic Policy Gradient) algorithm, which is a state-of-the-art deep reinforcement learning algorithm. We use a reward function and a deep neural network to build the controller. By using the proposed controller, a bicycle can not only be stably balanced but also travel to any specified location. We confirm that the controller with DDPG shows better performance than the other baselines such as Normalized Advantage Function (NAF) and Proximal Policy Optimization (PPO). For the performance evaluation, we implemented the proposed algorithm in various settings such as fixed and random speed, start location, and destination location.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2073-8994
2073-8994
DOI:10.3390/sym11020290