Research on Robot Path Planning Method Based on Deep Learning Q Algorithm

DOI

10.2991/978-94-6463-821-9_27How to use a DOI?

Keywords

Deep Learning; Path Planning; Q-Learning Algorithm; Prioritized Experience Replay; Causal Model

Abstract

With the rapid development of mobile robot technology, dynamic decision-making and path planning of robots in complex dynamic environments have become a current research hotspot. Due to its advantages in handling dynamic environments, the Q-learning algorithm in deep reinforcement learning has been widely applied to robot path planning tasks. This paper reviews the current applications of deep Q-learning algorithms in path planning. Then it analyzes the existing challenges, and proposes directions for further improvement. This paper concludes that in practical applications, deep Q-learning algorithms still face several challenges, such as the curse of dimensionality, low data efficiency, long decision delays, and a tendency to fall into local optima. To address these problems, researchers have proposed some improvement measures, including prioritizing experience replay, adding a noise layer, introducing causal models, and other strategies. In the future, when studying path planning in dynamic environments, the research findings of this paper provide valuable directions for further investigation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haochen Lu
PY  - 2025
DA  - 2025/08/31
TI  - Research on Robot Path Planning Method Based on Deep Learning Q Algorithm
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 240
EP  - 250
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_27
DO  - 10.2991/978-94-6463-821-9_27
ID  - Lu2025
ER  -