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

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...

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Bibliographic Details
Main Authors: SeungYoon Choi, Tuyen P. Le, Quang D. Nguyen, Md Abu Layek, SeungGwan Lee, TaeChoong Chung
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Symmetry
Subjects:
deep reinforcement learning
deep deterministic policy gradient (DDPG)
machine learning
self-driving bicycle
Online Access:https://www.mdpi.com/2073-8994/11/2/290
Description
Summary: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.
ISSN:2073-8994

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