Improving the learning efficiency of robot path planning via fuzzified deep reinforcement learning

Improving the learning efficiency of robot path planning via fuzzified deep reinforcement learning

Mobile robot navigation is a significant research challenge, especially in scenarios where efficient learning is crucial. Deep reinforcement learning (DRL) offers a promising alternative to traditional control strategies, yet its effectiveness is often limited by sparse reward structures and sim...

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Bibliographic Details
Main Authors: Al Mahmud, Suaib, Mohd Ibrahim, Azhar, Khan, Mazbahur Rahman, Saeed Baqalaql, Odai, De Wilde, Philippe
Format: Proceeding Paper
Language:en
Published: IEEE 2025
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/123895/7/123895_%20Improving%20the%20learning%20efficiency.pdf
http://irep.iium.edu.my/123895/
https://ieeexplore.ieee.org/document/11197609/authors#authors
https://doi.org/10.1109/FUZZ62266.2025.11197609
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http://irep.iium.edu.my/123895/7/123895_%20Improving%20the%20learning%20efficiency.pdf
http://irep.iium.edu.my/123895/
https://ieeexplore.ieee.org/document/11197609/authors#authors
https://doi.org/10.1109/FUZZ62266.2025.11197609

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