Abstract
AbstractDriven 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.
Publisher
Cambridge University Press (CUP)
Subject
Computer Science Applications,General Mathematics,Software,Control and Systems Engineering,Control and Optimization,Mechanical Engineering,Modeling and Simulation,Artificial Intelligence,Computer Vision and Pattern Recognition,Computational Mechanics,Rehabilitation
Cited by
8 articles.
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