Integrating Machine Learning for Optimal Path Planning

Shiru Xiao

doi:10.70393/6a6374616d.323534

OPEN ACCESS |Research Article ||1 January 2025

Integrating Machine Learning for Optimal Path Planning

Shiru Xiao

National University of Singapore

siruxiao0991@gmail.com

National University of Singapore, Singapore.

* Corresponding Author¹: Shiru Xiao, E-Mail: siruxiao0991@gmail.com

Publication

Accepted Unknow ; Published 2025 January 1

Journal of Computer Technology and Applied Mathematics, 2025, 2(1), 3007-4126.

Abstract

In the area of AI based path planning, the learner is not told which actions to take, as is common in most forms of machine learning. Instead, the learner must discover through trial and error, which actions yield the most rewards. In the most interesting and challenging cases, actions affect not only the immediate rewards but also the next station or subsequent rewards. The characteristics of trial and error searches and delayed reward are two important distinguishing features of RL, which are defined not by characterizing learning methods, but by characterizing a learning problem.

Keywords

Machine Learning , Robotic Vision , Path Planning .

Metadata

DOI:

10.70393/6a6374616d.323534

ARK:

ark:/40704/JCTAM.v2n1a04

Pages: 21-26

References: 22

Disciplines: Artificial Intelligence

Subjects: Machine Learning

Cite This Article

APA Style

Xiao, S. (2025). Integrating machine learning for optimal path planning. Journal of Computer Technology and Applied Mathematics, 2(1), 21-26. https://doi.org/10.70393/6a6374616d.323534

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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