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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Symmetry (Basel) Ročník 11; číslo 2; s. 290
Hlavní autoři: Choi, SeungYoon, Le, Tuyen P., Nguyen, Quang D., Layek, Md Abu, Lee, SeungGwan, Chung, TaeChoong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.02.2019
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2073-8994
2073-8994
DOI:10.3390/sym11020290