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Reinforcement learning with modified exploration strategy for mobile robot path planning

Published online by Cambridge University Press:  11 May 2023

Nesrine Khlif Open the ORCID record for Nesrine Khlif [Opens in a new window] ,
Khraief Nahla  and
Belghith Safya
Nesrine Khlif*
Affiliation:
Laboratory of Robotics, Informatics and Complex Systems (RISC lab - LR16ES07), ENIT, University of Tunis EL Manar, Le BELVEDERE, Tunis, Tunisia
Khraief Nahla
Affiliation:
Laboratory of Robotics, Informatics and Complex Systems (RISC lab - LR16ES07), ENIT, University of Tunis EL Manar, Le BELVEDERE, Tunis, Tunisia
Belghith Safya
Affiliation:
Laboratory of Robotics, Informatics and Complex Systems (RISC lab - LR16ES07), ENIT, University of Tunis EL Manar, Le BELVEDERE, Tunis, Tunisia
*
Corresponding author: Nesrine Khlif; Email: nesrine.khlif@etudiant-enit.utm.tn
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Abstract

Driven by the remarkable developments we have observed in recent years, path planning for mobile robots is a difficult part of robot navigation. Artificial intelligence applied to mobile robotics is also a distinct challenge; reinforcement learning (RL) is one of the most used algorithms in robotics. The exploration-exploitation dilemma is a motivating challenge for the performance of RL algorithms. The problem is balancing exploitation and exploration, as too much exploration leads to a decrease in cumulative reward, while too much exploitation locks the agent in a local optimum. This paper proposes a new path planning method for mobile robot based on Q-learning with an improved exploration strategy. In addition, a comparative study of Boltzmann distribution and $\epsilon$-greedy politics is presented. Through simulations, the better performance of the proposed method in terms of execution time, path length, and cost function is confirmed.

Keywords

mobile roboticpath planningQ-learningexploration-exploitation strategiesBoltzmann distributionϵ-greedy

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 9 , September 2023 , pp. 2688 - 2702
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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