Reinforcement learning with modified exploration strategy for mobile robot path planning

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...

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Bibliographic Details
Published in:Robotica 2023-09, Vol.41 (9), p.2688-2702
Main Authors: Khlif, Nesrine, Nahla, Khraief, Safya, Belghith
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Boltzmann distribution
Comparative studies
Cost function
Energy consumption
Exploitation
Machine learning
Methods
Navigation systems
Path planning
Planning
Robotics
Robots
Strategy
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Summary: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.
ISSN:0263-5747
1469-8668
DOI:10.1017/S0263574723000607